CN112542249A - Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics - Google Patents

Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics Download PDF

Info

Publication number
CN112542249A
CN112542249A CN202011271392.7A CN202011271392A CN112542249A CN 112542249 A CN112542249 A CN 112542249A CN 202011271392 A CN202011271392 A CN 202011271392A CN 112542249 A CN112542249 A CN 112542249A
Authority
CN
China
Prior art keywords
drug
record
culture
department
dividing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202011271392.7A
Other languages
Chinese (zh)
Inventor
霍瑞
林�建
陈春平
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Xinglin Information Technology Co ltd
Original Assignee
Hangzhou Xinglin Information Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Xinglin Information Technology Co ltd filed Critical Hangzhou Xinglin Information Technology Co ltd
Priority to CN202011271392.7A priority Critical patent/CN112542249A/en
Publication of CN112542249A publication Critical patent/CN112542249A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Epidemiology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Chemical & Material Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Medicinal Chemistry (AREA)
  • Databases & Information Systems (AREA)
  • Pharmacology & Pharmacy (AREA)
  • Toxicology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The invention provides a method, a device, equipment and a storage medium for synchronously detecting the times of multi-drug-resistant cases based on MapReduce and big data statistics. The invention can carry out massive parallel calculation on the big data of million-level, million-level and hundred million-level inpatients according to various calibers such as provincial and urban areas, hospital levels, hospital beds, comprehensive and special departments, public standings, civil camps and the like, has strong calculation practicability on the times of detecting multiple drug-resistant cases in inpatients, can comprehensively carry out accurate statistics on various multiple bacteria-resistant cases, and can provide effective guidance for the treatment and the drug selection of the inpatients.

Description

Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics
Technical Field
The invention belongs to the technical field of drug-resistant patient management, and particularly relates to a method, a device, equipment and a storage medium for synchronously detecting the times of multiple drug-resistant cases based on MapReduce and big data statistics, which are particularly suitable for a scene that the data volume of a patient to be processed far exceeds the storage (magnetic disc) of one server and the computing capacity (memory and CPU) cannot be manually split and distributed.
Background
Multidrug-resistant bacteria refer to pathogenic bacteria with multidrug resistance. In particular to a microorganism which can resist three or more types of antibiotics at the same time, but not three types of antibiotics.
The multiple drug-resistant bacteria comprise nine kinds, namely methicillin-resistant staphylococcus aureus, vancomycin-resistant enterococcus faecalis, vancomycin-resistant enterococcus faecium, three-four generation cephalosporin escherichia coli, carbapenem-resistant escherichia coli, three-four generation cephalosporin klebsiella pneumoniae, carbapenem-resistant acinetobacter baumannii and carbapenem-resistant pseudomonas aeruginosa. Wherein the methicillin resistance refers to drug resistance of pathogens to any one of cefoxitin, oxacillin and methicillin; the resistance to the cephalosporins is that the pathogens are resistant to any one of cefotaxime, ceftriaxone, ceftazidime and cefepime; carbapenems are resistance to any of imipenem, meropenem, doripenem, and ertapenem by pathogens.
Because the drug-resistant strain is resistant to various clinically and generally used antibacterial drugs, the treatment after infection is difficult, and the fatality rate is high. The drug-resistant strains are wide in distribution, rapid in spread and easy to generate epidemic outbreaks, and bring difficulty to clinical treatment and control of nosocomial infection. In fact, with the unjustified use and abuse of antibacterial agents, the resistance rates of microorganisms are increasing. Therefore, the method has important significance for managing hospital infection multi-drug resistant bacteria.
The number of cases of detecting the multi-drug resistant bacteria causing the nosocomial infection in the inpatients in the same period refers to the number of cases of detecting the multi-drug resistant bacteria causing the nosocomial infection in the inpatients in a specified period of time in the same period. The conventional management of the hospital infection multi-drug-resistant bacteria cannot manage the times of the cases of the multi-drug-resistant bacteria, only can record a single case of the multi-drug-resistant bacteria, cannot realize the integral prevention, control and management of the hospital infection multi-drug-resistant bacteria, and cannot integrally evaluate the hospital infection condition. The detected multi-drug-resistant bacteria cases are manually recorded, and the system processing efficiency is low.
Therefore, how to realize the number of times of synchronously detecting multiple drug-resistant bacteria cases causing nosocomial infection of inpatients in a specified time period becomes a problem to be solved urgently.
The times of detecting multiple drug resistance cases in the same period are relatively easy to calculate in one medical institution, the number of people discharged from one common medical institution such as the third-class A is about fifty thousands of people every year, and the state or provincial-class tap hospital has hundreds of thousands of people. The calculation of the key indexes is complex under the condition of large data of millions, billions and billions of inpatients of hundreds and thousands of medical institutions in provincial regions or nationwide ranges, 2749 third-level hospitals in the China in 2019, 9687 second-level hospitals in 2019 and 17487 thousands of inpatients in public hospitals, and the initial result of one-time statistical analysis needs to be calculated in the last year.
Therefore, how to develop standardization, standardization and homogenization hospital infection monitoring in hundreds of hospitals and thousands of hospitals in one area and realize the number of times of detecting multiple drug resistance cases of inpatients in a specified time period under the condition of big data of the inpatients becomes the most urgent problem to be solved for developing a regional information monitoring platform.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a storage medium for synchronously detecting the times of multiple drug resistance cases based on MapReduce and big data statistics. The method has strong practicability for counting the times of detecting multiple drug resistant cases in the inpatients, can comprehensively and accurately count various multiple drug resistant cases under the condition of big data of the inpatients, and can provide effective guidance for the treatment and the drug selection of the inpatients. Meanwhile, the problem that manual multi-drug resistant bacterium case management processing is complex is solved by automatically counting the times of the multi-drug resistant cases.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for synchronously detecting the times of multiple drug resistant cases based on MapReduce and big data statistics comprises the following steps:
s1, receiving the statistical time and department selected by the user, and determining the authority department of the user according to the identity information of the user;
s2, collecting patient' S information B of transferring department, judging whether there is transferring record of time crossing with the statistical time and department belonging to the authority department and the selected department, if yes, executing step S3, if not, outputting the number of times of detecting multiple drug resistant cases in the inpatient as 0;
s3, acquiring hospitalization process information A of the patient, and acquiring the hospitalization time and the discharge time of the patient based on the hospitalization process information, wherein the hospitalization time and the discharge time are jointly used as parameters g.MC2;
s4, collecting a bacterial culture record J, dividing the bacterial culture record J into bacterial culture records J (a) _ Y submitted during patient hospitalization and J (a) _ N not submitted during patient hospitalization based on the parameter g.mc2;
s5, obtaining a bacterial culture record J (d) Y meeting the statistical time, the authority department and the selection limit from the bacterial culture records J (a) Y;
s6, dividing the bacterial culture record J (d) Y into a bacterial culture record J (e) Y with an infection type not polluted and a bacterial culture record J (e) N with an infection type polluted;
s7, acquiring a test number list parameter g.MRO based on the bacterial culture record J (e) Y;
s8, collecting a drug sensitivity test record K, and dividing the drug sensitivity test record K into a drug sensitivity test record K (a) Y in a specified test number range and a drug sensitivity test record K (a) N not in the specified test number range based on a parameter g.MRO;
s9, dividing the susceptibility test record K (a) Y into the culture results of Escherichia coli or Klebsiella pneumoniae, and dividing the susceptibility test record K (c1) Y of susceptibility drugs in the range of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime and ceftazidime;
s10, dividing the drug sensitive test record K (a) Y into a drug sensitive test record K (c2) Y with a culture result of enterococcus faecalis or enterococcus faecium and a drug sensitive drug of vancomycin;
s11, dividing the drug sensitive test record K (a) Y into culture results of Acinetobacter baumannii or Pseudomonas aeruginosa, and dividing the drug sensitive test record K (c3) Y of drug sensitive drugs in the range of imipenem, meropenem and doripenem;
s12, dividing the drug sensitive test record K (a) Y into a culture result of staphylococcus aureus and a drug sensitive test record K (c4) Y of drug sensitive drugs in the range of cefoxitin, oxacillin and methicillin;
s13, merging the K (c1) Y, K (c2) Y, K (c3) Y, K (c4) Y to obtain a drug sensitivity test record K (d), and dividing the drug sensitivity test record K (d) into a drug sensitivity test record K (e) with a drug sensitivity result of intermediary or drug resistance and a drug sensitivity test record K (e) with a drug sensitivity result of no intermediary or drug resistance, wherein the drug sensitivity test record K (d) is divided into a drug sensitivity test record K (e) with a drug sensitivity result of intermediary or drug resistance and a drug sensitivity test record K (e) N;
and S14, outputting the times of the detection of multiple drug resistance cases in the hospitalized patients based on the number recorded in the drug susceptibility test record K (e) Y.
Further, the information of the branch department comprises the patient case number, the department, the time of entering the department and the time of leaving the department; the hospitalization process information comprises a patient case number, an admission department, admission time, a discharge department and discharge time; the bacterial culture record comprises a patient case number, a submission department, a project name, sampling time, report time, a culture result, a sample number and a type; the drug sensitivity test records comprise patient case numbers, inspection departments, sampling time, culture results, sample numbers, drug sensitivity medicines and drug sensitivity results.
Further, the step S2 includes:
s21, collecting the patient' S information B of the branch department, dividing the information B of the branch department into information B (a) and Y of the branch department whose time is crossed with the statistical time and information B (a) and N of the branch department whose time is not crossed with the statistical time;
s22, dividing the branch information B (a) _ Y into branch information B (b) _ Y of which the department belongs to the authority department and branch information B (b) _ N of which the department does not belong to the authority department based on the authority department;
s23, dividing the branch information B (b) _ Y into branch information B (c) _ Y of which the department belongs to the selected department and branch information B (c) _ N of which the department does not belong to the selected department based on the selected department;
s24, judging whether the branch information B (c) and Y has branch records, if yes, executing step S3, and if not, outputting the number of times of detecting multiple drug resistance cases in the hospitalized patient as 0.
Further, the step S5 includes:
s51, dividing the bacterial culture record J (a) Y into a bacterial culture record J (b) Y which is checked at the statistical time and a bacterial culture record J (b) N which is not in the statistical time range;
s52, dividing the bacteria culture record J (b) Y into a checked bacteria culture record J (c) Y in the user authority department range and a checked bacteria culture record J (c) N out of the user authority department range based on the authority department;
s53, based on the selected department, dividing the bacterial culture record J (c) _ Y into the bacterial culture record J (d) _ Y for the delivery in the delivery department selected by the user and the bacterial culture record J (d) _ N for the delivery not in the delivery department.
Further, the step S9 includes:
s91, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b1) Y with the culture result of Escherichia coli and Klebsiella pneumoniae and a drug sensitivity test record K (b1) N with the culture result of not Escherichia coli and Klebsiella pneumoniae based on the culture result;
s92, based on the susceptibility drug, assigning the susceptibility test record K (b1) _ Y to susceptibility test records K (c1) _ Y of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime, and performing susceptibility test records K (c1) _ N of other susceptibility drugs;
the step S10 includes:
s101, based on the culture result, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b2) Y with the culture result of enterococcus faecalis and enterococcus faecium and a drug sensitivity test record K (b2) N with the culture result of not enterococcus faecalis and enterococcus faecium;
s102, dividing the drug susceptibility test record K (b2) _ Y into a drug susceptibility test record K (c2) _ Y of vancomycin and performing a drug susceptibility test record K (c2) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;
the step S11 includes:
s111, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b3) Y with a culture result of Acinetobacter baumannii or Pseudomonas aeruginosa and a drug sensitivity test record K (b3) N with a culture result of Acinetobacter baumannii or Pseudomonas aeruginosa based on the culture result;
s112, based on the drug sensitive drugs, dividing the drug sensitive test records K (b3) _ Y into the drug sensitive test records K (c3) _ Y of the drug sensitive drugs in the ranges of imipenem, meropenem and doripenem, and carrying out the drug sensitive test records K (c3) _ N of other drug sensitive drugs;
the step S12 includes:
s121, dividing the susceptibility test record K (a) Y into a susceptibility test record K (b4) Y with a culture result of Staphylococcus aureus and a susceptibility test record K (b4) N with a culture result of not Staphylococcus aureus based on the culture result;
s122, based on the drug sensitive drugs, dividing the drug sensitive test record K (b4) _ Y into the drug sensitive test records K (c4) _ Y of the drug sensitive drugs in the ranges of the Sphaestin, the oxacillin and the methicillin, and carrying out the drug sensitive test records K (c4) _ N of other drug sensitive drugs.
The invention also provides a device for synchronously detecting the times of the multiple drug resistance cases based on MapReduce and big data statistics, which comprises the following steps:
the receiving unit is used for receiving the statistical time and department selected by the user and determining the authority department of the user according to the identity information of the user;
the collection and judgment unit is used for collecting the patient's branch information B, judging whether branch records of time and statistical time are crossed and departments simultaneously belong to the authority department and the selected department exist in the branch information B, calling the first parameter acquisition unit if the branch records exist, and outputting the number of times of detecting multiple drug resistance cases in the inpatient to be 0 if the branch records do not exist;
the system comprises a first parameter acquisition unit, a second parameter acquisition unit and a third parameter acquisition unit, wherein the first parameter acquisition unit is used for acquiring hospitalization process information A of a patient, and acquiring the hospitalization time and the discharge time of the patient as a parameter g.MC2 together based on the hospitalization process information;
a bacterial culture record first dividing unit for acquiring a bacterial culture record J, dividing the bacterial culture record J into bacterial culture records J (a) Y to be submitted during hospitalization of the patient and bacterial culture records J (a) N not to be submitted during hospitalization of the patient based on the parameter g.mc2;
the bacteria culture record dividing unit is used for acquiring the bacteria culture records J (d) Y meeting the statistical time, the authority department and the selection limit in the bacteria culture records J (a) Y;
a bacteria culture record fifth dividing unit for dividing the bacteria culture record j (d) Y into a bacteria culture record j (e) Y whose infection type is not contaminated and a bacteria culture record j (e) N whose infection type is contaminated;
the second parameter acquisition unit is used for acquiring a test number list parameter g.MRO based on the bacterial culture record J (e) _ Y;
the drug susceptibility test record first dividing unit is used for collecting a drug susceptibility test record K, and dividing the drug susceptibility test record K into a drug susceptibility test record K (a) Y in a specified test number range and a drug susceptibility test record K (a) N out of the specified test number range based on a parameter g.MRO;
a susceptibility test record dividing unit for dividing the susceptibility test record K (a) Y into susceptibility test records K (c1) Y with a culture result of Escherichia coli or Klebsiella pneumoniae in the range of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime;
the fourth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) _ Y into the drug susceptibility test record K (c2) _ Y of which the culture result is enterococcus faecalis or enterococcus faecium and the drug susceptibility drug is vancomycin;
a seventh division unit for dividing the drug susceptibility test record K (a) _ Y into the culture results of Acinetobacter baumannii or Pseudomonas aeruginosa, and the drug susceptibility test records K (c3) _ Y of drug susceptibility drugs in the ranges of imipenem, meropenem and doripenem;
the tenth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) _ Y into the drug susceptibility test record K (c4) _ Y with the culture result of staphylococcus aureus and the drug susceptibility drug in the ranges of Sphaestin, oxacillin and methicillin;
a merging unit, configured to merge the K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain a drug sensitivity test record K (d), and divide the drug sensitivity test record K (d) into a drug sensitivity test record K (e) with a drug sensitivity result of intermediate or drug resistance and a drug sensitivity test record K (e) with a drug sensitivity result of non-intermediate or drug resistance based on the drug sensitivity result;
and the output unit is used for outputting the times of the detected multiple drug resistance cases in the hospitalized patients based on the number recorded in the drug susceptibility test records K (e) Y.
Further, the collecting and judging unit includes:
a first division unit of the branch information, which is used for collecting the branch information B of the patient, and dividing the branch information B into branch information B (a) Y with the time crossed with the statistical time and branch information B (a) N with the time not crossed with the statistical time;
a subject information second dividing unit configured to divide the subject information b (a) _ Y into subject information b (b) _ Y whose subject belongs to the authority subject room and subject information b (b) _ N whose subject does not belong to the authority subject room, based on the authority subject room;
a third division unit of the department information, which is used for dividing the department information B (b) _ Y into the department information B (c) _ Y of which the department belongs to the selected department and the department information B (c) _ N of which the department does not belong to the selected department based on the selected department;
a judging unit, which is used for judging whether the branch information B (c) _ Y has a branch record, if yes, the first parameter obtaining unit is called, and if not, the frequency of detecting multiple drug resistance cases in the hospitalized patient is output as 0;
the bacteria culture record dividing unit comprises:
a second division unit for dividing the bacteria culture record J (a) Y into bacteria culture records J (b) Y for censoring at a statistical time and bacteria culture records J (b) N not within the statistical time range;
a third division unit of bacteria culture records, which is used for dividing the bacteria culture records J (b) _ Y into bacteria culture records J (c) _ Y for censorship in the range of the user authority department and bacteria culture records J (c) _ N for censorship out of the range based on the authority department;
and a bacteria culture record fourth dividing unit for dividing the bacteria culture record J (c) _ Y into a bacteria culture record J (d) _ Y for censorship in the censorship department selected by the user and a bacteria culture record J (d) _ N for censorship in a range not in the censorship department based on the selected department.
Further, the drug sensitivity test record dividing unit comprises:
a second division unit for dividing the susceptibility test record K (a) _ Y into a susceptibility test record K (b1) _ Y and a susceptibility test record K (b1) _ N, based on the culture result, the culture result being Escherichia coli and Klebsiella pneumoniae, the culture result being other than Escherichia coli and Klebsiella pneumoniae;
a susceptibility test record third dividing unit for dividing the susceptibility test record K (b1) _ Y into susceptibility test records K (c1) _ Y for imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime, and performing susceptibility test records K (c1) _ N for other susceptibility drugs, based on the susceptibility drug;
the fourth dividing unit of the drug susceptibility test record comprises:
a fifth division unit for dividing the test record K (a) Y into test records K (b2) Y whose culture results are enterococcus faecalis and enterococcus faecium and test records K (b2) N whose culture results are not enterococcus faecalis and enterococcus faecium;
the drug susceptibility test record sixth dividing unit is used for dividing the drug susceptibility test record K (b2) _ Y into a drug susceptibility test record K (c2) _ Y of vancomycin and a drug susceptibility test record K (c2) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;
the seventh dividing unit of the drug susceptibility test record comprises:
the eighth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) Y into a drug susceptibility test record K (b3) Y with the culture result of Acinetobacter baumannii or Pseudomonas aeruginosa and a drug susceptibility test record K (b3) N with the culture result of Acinetobacter baumannii or Pseudomonas aeruginosa based on the culture result;
the ninth division unit for dividing the drug susceptibility test record K (b3) _ Y into the drug susceptibility test records K (c3) _ Y of the drug susceptibility drugs in the ranges of imipenem, meropenem and doripenem and carrying out the drug susceptibility test records K (c3) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;
the tenth division unit of the drug susceptibility test record comprises:
a susceptibility test record eleventh dividing unit for dividing the susceptibility test record K (a) _ Y into a susceptibility test record K (b4) _ Y whose culture result is staphylococcus aureus and a susceptibility test record K (b4) _ N whose culture result is not staphylococcus aureus, based on the culture result;
and the drug susceptibility test record twelfth dividing unit is used for dividing the drug susceptibility test record K (b4) _ Y into the drug susceptibility test record K (c4) _ Y of the drug susceptibility drug in the ranges of the Sphaestin, the oxacillin and the methicillin and carrying out the drug susceptibility test record K (c4) _ N of other drug susceptibility drugs on the basis of the drug susceptibility drugs.
The invention also provides computer equipment, which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor realizes the method for synchronously detecting the times of the multiple drug resistance cases based on MapReduce and big data statistics when executing the computer program.
The invention also provides a storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for synchronously detecting the times of the multiple drug resistance cases based on MapReduce and big data statistics can be realized.
The invention discloses a specific implementation mode for synchronously detecting the times of multiple drug resistant cases based on MapReduce and big data statistics, which is characterized in that the specific implementation mode is used for determining the times of the multiple drug resistant cases based on MapReduce and big data statistics, the hospital authority case of a user is determined according to the identity information of the user by utilizing hospitalization process information, branch information, bacterial culture information, drug sensitive test information, selected statistical time and selected case, the times of the multiple drug resistant cases of different types causing hospital infection of inpatients in a specified time period are determined respectively based on different multiple drug resistant bacteria and corresponding drug sensitive medicines, and the times of the multiple drug resistant cases of different types are combined to obtain the final times of the cases. The method comprises the steps of defining a map function with specific steps for calculating the times of cases of hospital infection with multi-drug-resistant bacteria by fully utilizing the parallel computing capability of a machine under a distributed system through a general parallel computing framework based on a MapReduce concept, processing key/value pairs of diagnosis and treatment information and computing results of inpatients through the map function, and outputting a plurality of intermediate format key/value pairs; a reduce function is then defined to merge the values corresponding to the same intermediate key. Therefore, a large task of calculating the times of cases of hospital infection with multi-drug-resistant bacteria by millions and millions of inpatients beyond the memory and storage limit of a server can be divided into tens of millions and hundreds of millions of small tasks, then the small tasks are executed on a plurality of machines simultaneously, and the intermediate output results of the small tasks are summarized to generate the final result. The invention can carry out massive parallel calculation on the large data which can not be avoided when a national level and provincial level monitoring network is constructed and contains million, million and hundred million levels of inpatients according to provincial and urban areas, hospital levels, hospital beds, comprehensive and special departments, various calibers such as public and private camps and the like. Meanwhile, the problem that manual multi-drug resistant bacterium case management processing is complex is solved by automatically counting the times of the multi-drug resistant cases.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for synchronously detecting the number of multiple drug resistance cases based on MapReduce and big data statistics, provided by an embodiment of the present invention;
FIG. 2 is a schematic sub-flow chart of a method for synchronously detecting the number of multi-drug resistance cases based on MapReduce and big data statistics according to an embodiment of the present invention;
FIG. 3 is a schematic sub-flow chart of a method for synchronously detecting the number of multi-drug resistance cases based on MapReduce and big data statistics according to an embodiment of the present invention;
FIG. 4 is a schematic sub-flow chart of a method for synchronously detecting the number of multi-drug resistance cases based on MapReduce and big data statistics according to an embodiment of the present invention;
FIG. 5 is a schematic sub-flow chart of a method for synchronously detecting the number of multi-drug resistance cases based on MapReduce and big data statistics according to an embodiment of the present invention;
FIG. 6 is a schematic sub-flow chart of a method for synchronously detecting the number of multi-drug resistance cases based on MapReduce and big data statistics according to an embodiment of the present invention;
FIG. 7 is a schematic sub-flow chart of a method for synchronously detecting the number of multi-drug resistance cases based on MapReduce and big data statistics according to an embodiment of the present invention;
fig. 8 is a schematic block diagram of units of an apparatus for synchronously detecting the number of multiple drug resistance cases based on MapReduce and big data statistics, according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In the following examples, X (y) type specification:
x represents a data set of a certain type;
y represents a serial number used for distinguishing a front data set and a rear data set of the same type of data in different logic units;
x (y) represents a data set under different logical units for a certain type of data;
y represents a coincidence condition;
n represents nonconforming;
the embodiment provides a method for synchronously detecting the times of multiple drug resistance cases based on MapReduce and big data statistics, and the statistical method is applied to a server, for example, a cloud server. The server obtains hospital data. Hospital data is processed. As shown in fig. 1, the statistical method for the number of times of the multiple drug resistance cases detected in the hospitalized patients includes the following steps S1 to S14:
s1, receiving the statistical time and department selected by the user, and determining the authority department of the user according to the identity information of the user;
the frequency of the cases with multiple drug resistance detected in the hospitalized patients is the frequency of the cases with specific multiple drug resistant bacteria detected in the same period in the hospitalized patients in the determined period. The multiple drug-resistant bacteria comprise nine kinds, namely methicillin-resistant staphylococcus aureus, vancomycin-resistant enterococcus faecalis, vancomycin-resistant enterococcus faecium, third-and fourth-generation cephalosporin-resistant Escherichia coli, third-and fourth-generation cephalosporin-resistant Klebsiella pneumoniae, carbapenem-resistant Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenem-resistant Acinetobacter baumannii and carbapenem-resistant Pseudomonas aeruginosa.
Wherein the methicillin resistance refers to drug resistance of pathogens to any one of cefoxitin, bensulin and methicillin; the resistance to the cephalosporins is that the pathogens are resistant to any one of cefotaxime, ceftriaxone, ceftazidime and cefepime; carbapenems are resistance to any of imipenem, meropenem, doripenem, and ertapenem by pathogens.
The patients synchronously detecting specific multiple drug-resistant bacteria based on MapReduce and big data statistics need to meet the following requirements: 1. the patient's stay in the hospital is within the statistical time frame. That is, the time period formed by the admission time and the discharge time of the patient is crossed with the statistical time; 2. more than two kinds of multi-drug resistant bacteria are detected from the same specimen, and corresponding results need to be calculated respectively; 3. the contamination type of multiple drug-resistant bacteria is not counted; 4. the patient is checked to detect multi-drug resistant bacteria during hospitalization; 5. the condition of the selection of the user is satisfied.
Therefore, the invention is used for automatic management of the times of detecting multi-drug resistance cases in the inpatients, so that the user is required to select a corresponding time period, namely the user selects corresponding statistical time to perform statistics and search on the inpatients. In addition, for the inpatients, the user usually manages the inpatients according to specific departments, so that the invention also sets corresponding departments besides the statistical time. The hospital data has corresponding privacy, so that the statistics and management of the hospital data in the invention require a user to acquire corresponding data authority. The data authority of the user is associated with the corresponding identity information, so that the invention determines the authority department of the user according to the identity information of the operation user, and carries out statistics and management on the times of detecting multi-drug resistance examples of the data in the authority department.
S2, collecting patient' S information B of transferring department, judging whether there is transferring record of time crossing with the statistical time and department belonging to the authority department and the selected department, if yes, executing step S3, if not, outputting the number of times of detecting multiple drug resistant cases in the inpatient as 0;
the branch information is used for recording the information of entering and leaving the department of each diagnosis and treatment department during the hospitalization period of the patient, and specifically comprises the patient case number, the department, the time of entering the department, the time of leaving the department and the like. For the branch information B, the branch information is firstly screened based on statistical time, an authority department and a selected department, and only if corresponding branch records exist after screening, the possibility that the inpatients with multiple drug resistance are detected exists. Therefore, when there is no referral record after screening, that is, there is no department requirement satisfying the statistical time, the authority department and the selection at the same time, the number of times of the detected multi-drug resistance cases in the inpatients is 0, that is, there is no inpatient in which the multi-drug resistance is detected. In the present invention, the subject information is sequentially filtered based on the statistical time, the authority subject and the selected subject, and therefore, in an embodiment, referring to fig. 2, the step S2 may include steps S21 to S24.
S21, collecting the patient' S information B of the branch department, dividing the information B of the branch department into information B (a) and Y of the branch department whose time is crossed with the statistical time and information B (a) and N of the branch department whose time is not crossed with the statistical time;
the invention firstly screens the information B of the department transfer based on the statistical time, wherein the information B is the initial data set of the type of the department transfer of the corresponding patient. Y represents a qualified branch record, and N represents an unqualified branch record. The fact that the time is intersected with the statistical time means that the statistical time belongs to a time period when the patient is in the corresponding department, namely the statistical time is located between the time of entering the department and the time of leaving the department when the patient is in the corresponding department, and otherwise, the time is not intersected with the statistical time.
For example, the referral information B is:
patient's case number Department's office Time of entering the clinic Time of delivery
123456(1) Neurology department 2019-01-01 00:00:12 2019-01-05 01:00:12
123456(1) ICU 2019-01-05 01:00:12 2019-01-08 02:00:12
123456(1) Rehabilitation department 2019-01-08 02:00:12 2019-01-12 03:00:12
The statistical time is [ 2019-01-0100: 00:00,2019-01-1023: 59:59], then B (a) Y is:
patient's case number Department's office Time of entering the clinic Time of delivery
123456(1) Neurology department 2019-01-01 00:00:12 2019-01-05 01:00:12
123456(1) ICU 2019-01-05 01:00:12 2019-01-08 02:00:12
123456(1) Rehabilitation department 2019-01-08 02:00:12 2019-01-12 03:00:12
B (a) N is:
patient's case number Department's office Time of entering the clinic Time of delivery
S22, dividing the branch information B (a) _ Y into branch information B (b) _ Y of which the department belongs to the authority department and branch information B (b) _ N of which the department does not belong to the authority department based on the authority department;
because the authority of each user is different, the invention screens the branch information B (a) Y based on the authority department room, so that the data operated by the user is adaptive to the corresponding authority. And comparing the 'department' field in the branch information with the authority department, and judging whether the 'department' field belongs to the scope of the authority department. The department information b (b) _ Y is a department record in a department belonging to the authority range managed by the user, and the department information b (b) _ N is a department record in a department not belonging to the authority range managed by the user.
For example, the rights department is: all departments, for the above b (a) _ Y, b (b) _ Y:
patient's case number Department's office Time of entering the clinic Time of delivery
123456(1) Neurology department 2019-01-01 00:00:12 2019-01-05 01:00:12
123456(1) ICU 2019-01-05 01:00:12 2019-01-08 02:00:12
123456(1) Rehabilitation department 2019-01-08 02:00:12 2019-01-12 03:00:12
B (b) N is:
patient's case number Department's office Time of entering the clinic Time of delivery
S23, dividing the branch information B (b) _ Y into branch information B (c) _ Y of which the department belongs to the selected department and branch information B (c) _ N of which the department does not belong to the selected department based on the selected department;
in the invention, the user can manage the inpatients aiming at a specific department, therefore, the invention screens the department information B (b) _ Y based on the selected department, so that the statistical and screening data is adaptive to the department selected by the user independently, the user can select the corresponding data according to the requirement, and the cases of detecting multiple drug resistance in the inpatients of a specific department are counted. And comparing the 'department' field in the branch information with the selected department, and judging whether the 'department' field belongs to the range of the selected department.
For example, the department selected by the user is ICU, and for b (b) _ Y, b (c) _ Y described above:
Figure BDA0002777804130000101
Figure BDA0002777804130000111
b (c) N is:
patient's case number Department's office Time of entering the clinic Time of delivery
123456(1) Rehabilitation department 2019-01-08 02:00:12 2019-01-12 03:00:12
123456(1) Neurology department 2019-01-01 00:00:12 2019-01-05 01:00:12
S24, judging whether the branch information B (c) and Y has branch records, if yes, executing step S3, and if not, outputting the number of times of detecting multiple drug resistance cases in the hospitalized patient as 0.
Specifically, the invention judges according to the branch records B (c) _ Y, if the patient has records after the three steps, the operation is continued downwards, if the patient has no records, the operation is ended, and the result is 0.
For example, for b (c) _ Y described above, one record is included, so execution of step S3 is continued.
S3, acquiring hospitalization process information A of the patient, and acquiring the hospitalization time and the discharge time of the patient based on the hospitalization process information, wherein the hospitalization time and the discharge time are jointly used as parameters g.MC2;
the hospitalization process information is used for integrally recording the hospitalization process of the patient, and specifically comprises the patient case number, the hospital admission department, the hospital admission time, the hospital discharge department and the hospital discharge time. The method comprises the steps of firstly obtaining the hospitalization process information A of a patient, and further obtaining the relevant information of the fields of the admission time and the discharge time, wherein the relevant information is jointly used as the parameter g.MC2.
For example, hospitalization procedure information a is:
patient's case number Admission department Time of admission Discharge department Time of discharge
123456(1) Neurology department 2019-01-01 00:00:12 Rehabilitation department 2019-01-12 03:00:12
The obtained parameter g.mc2 is: [2019-01-0100:00:12,2019-01-1203:00:12].
S4, collecting a bacterial culture record J, dividing the bacterial culture record J into bacterial culture records J (a) _ Y submitted during patient hospitalization and J (a) _ N not submitted during patient hospitalization based on the parameter g.mc2;
the bacterial culture record is used for recording the culture process and the culture result of bacterial culture, and specifically comprises a patient case number, a submission department, a project name, sampling time, report time, a culture result, a sample number and a type. Normally, the sampling time of bacterial culture of a patient who detects specific multi-drug resistant bacteria is within the hospitalization time range of the patient, so the invention screens obviously wrong data according to the parameter g.MC2. Specifically, the invention filters out the bacterial culture records J (a) and N which are not checked during the hospitalization period of the patient at the sampling time based on the comparison of the 'sampling time' field in the bacterial culture records with the parameter g.MC2 of the hospitalization and discharge time, and obtains a bacterial culture record J (a) and Y with the sampling time within the hospitalization time range.
For example, bacterial culture record J is:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Staphylococcus aureus Whole blood 968584 HA
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Pseudomonas aeruginosa Whole blood 968584 Pollution (b) by
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Acinetobacter baumannii Whole blood 868485 Planting
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Enterococcus faecium Whole blood 584995 HA
For g.mc2 above, j (a) _ Y is:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Staphylococcus aureus Whole blood 968584 HA
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Pseudomonas aeruginosa Whole blood 968584 Pollution (b) by
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Acinetobacter baumannii Whole blood 868485 Planting
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Enterococcus faecium Whole blood 584995 HA
J (a) N is:
patient's case number Inspection department ItemName (R) Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
S5, obtaining a bacterial culture record J (d) Y meeting the statistical time, the authority department and the selection limit from the bacterial culture records J (a) Y;
in an embodiment of the present invention, referring to fig. 3, the step S5 includes steps S51-S53.
S51, dividing the bacterial culture record J (a) Y into a bacterial culture record J (b) Y which is checked at the statistical time and a bacterial culture record J (b) N which is not in the statistical time range;
the invention firstly screens the bacterial culture record J (a) Y based on the statistical time, and the submission time is crossed with the statistical time, namely the submission time is in the statistical time range. And comparing the 'sampling time' field in the bacterial culture record with the selected statistical time, and judging whether the 'sampling time' field belongs to the selected statistical time range.
For example, for j (a) _ Y and statistical time described above, j (b) _ Y is:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Staphylococcus aureus Whole blood 968584 HA
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Pseudomonas aeruginosa Whole blood 968584 Pollution (b) by
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Acinetobacter baumannii Whole blood 868485 Planting
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Enterococcus faecium Whole blood 584995 HA
J (b) N is:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
S52, based on the authority department, dividing the bacteria culture record J (b) _ Y into the bacteria culture record J (c) _ Y for delivery in the user authority department and the bacteria culture record J (c) _ Y for delivery out of the user authority department
Breeding record J (c) N;
because the authority of each user is different, the invention screens the bacteria culture record J (b) _ Y based on the authority department, so that the data operated by the user is adaptive to the corresponding authority. And comparing the 'submission department' field in the bacterial culture record with the authority department, and judging whether the 'submission department' field belongs to the scope of the authority department. The bacterial culture records J (c) _ Y are bacterial culture records belonging to the submission in the authority range managed by the user, and the bacterial culture records J (c) _ N are bacterial culture records not belonging to the submission managed by the user.
For example, the rights department is: all departments, for the above J (b) Y, J (c) Y are:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Staphylococcus aureus Whole blood 968584 HA
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Pseudomonas aeruginosa Whole blood 968584 Pollution (b) by
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Acinetobacter baumannii Whole blood 868485 Planting
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Enterococcus faecium Whole blood 584995 HA
J (c) and N are:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
S53, dividing the bacterial culture record J (c) _ Y into bacterial culture records J (d) _ Y for delivery in the delivery department selected by the user and bacterial culture records J (d) _ N for delivery out of the delivery department based on the selected department;
in the invention, a user can manage multi-drug resistance cases aiming at a specific department, so that the invention screens the bacterial culture records J (c) Y based on the selected department, ensures that the statistical and screened data are adaptive to the department selected by the user independently, ensures that the user can select the corresponding data as required, and counts the cases of detecting multi-drug resistance in inpatients in a specific department. The 'inspection department' field in the bacteria culture record is compared with the selected department, and whether the 'inspection department' field belongs to the selected department range is judged.
For example, the department of delivery selected by the user is ICU, and j (c) _ Y and j (d) _ Y are:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Staphylococcus aureus Whole blood 968584 HA
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Pseudomonas aeruginosa Whole blood 968584 Pollution (b) by
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Acinetobacter baumannii Whole blood 868485 Planting
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Enterococcus faecium Whole blood 584995 HA
J (d) N is:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
S6, dividing the bacterial culture record J (d) Y into a bacterial culture record J (e) Y with an infection type not polluted and a bacterial culture record J (e) N with an infection type polluted;
specifically, the present invention screens bacterial culture records J (d) _ Y based on the "type" field in the bacterial culture record. When the "type" field is not contaminated, it belongs to the bacterial culture record J (e) which is not contaminated in the infection type, otherwise it belongs to the bacterial culture record J (e) which is contaminated in the infection type. In actual business, due to the influence of the sample submission time and the sampling and storing modes, the culture result of the microorganism may have errors, and the submission information of the part of the microorganism cannot be regarded as valid data and is regarded as a contaminated sample and is not counted. Thus, the present invention culls the type of infection as the impact of contamination on the data.
For J (d) Y, J (e) Y mentioned above:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Staphylococcus aureus Whole blood 968584 HA
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Enterococcus faecium Whole blood 584995 HA
J (e) N is:
patient's case number Inspection department Name of item Sampling time Time of report Results of the culture Specimen (variants) Sample number Type (B)
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Pseudomonas aeruginosa Whole blood 968584 Pollution (b) by
123456(1) ICU Blood culture 2019-01-05 10:17:00 2019-01-08 09:15:00 Acinetobacter baumannii Whole blood 868485 Planting
S7, acquiring a test number list parameter g.MRO based on the bacterial culture record J (e) Y;
according to the bacterial culture record J (e) Y, the test number list parameter g.MRO corresponding to the bacterial culture is selected and obtained. The format of the test number is: sample number-culture result. The invention obtains each test number in the bacterial culture record J (e) Y, and all different test numbers jointly form a test number list parameter g.MRO. Specifically, the contents of the "sample number" and "culture result" fields in the bacterial culture record j (e) _ Y are extracted as test numbers in common.
For J (e) Y above, the parameter g.MRO is [ 968584-Staphylococcus aureus, 584995-enterococcus faecium ].
S8, collecting a drug sensitivity test record K, and dividing the drug sensitivity test record K into a drug sensitivity test record K (a) Y in a specified test number range and a drug sensitivity test record K (a) N not in the specified test number range based on a parameter g.MRO;
the drug sensitivity test result is used for recording the output result of the drug sensitivity test, and specifically comprises a patient case number, a submission department, sampling time, a culture result, a sample number, a drug sensitivity medicament and a drug sensitivity result.
The invention relates the drug sensitivity test record and the bacteria culture record, therefore, firstly, the drug sensitivity test record K is screened based on the parameter g.MRO in the bacteria culture record, and the drug sensitivity test record K (a) Y in the specified test number range and the drug sensitivity test record K (a) N not in the specified test number range are obtained by filtering, thus obtaining the drug sensitivity test record corresponding to the specific parameter g.MRO. Specifically, the fields of 'sample number' and 'culture result' in the drug sensitivity test record are obtained, and the drug sensitivity test record with the inconsistent 'sample number' and 'culture result' with g.MRO is filtered.
For example, the drug susceptibility test information K is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefoxitin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Penicillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Golden YellowStaphylococcus chromogenes 968584 Vancomycin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Oxacillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Tetracycline derivatives Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Erythromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Rifampicin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Azithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clindamycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Compound sulfamethoxazole Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clarithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Latamoxef Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Moxifloxacin hydrate Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Minocycline Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Ciprofloxacin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Meropenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Cefuroxime Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Vancomycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Penicillin Drug resistance
Mro, k (a) _ Y for the above parameters g.mro:
Figure BDA0002777804130000141
Figure BDA0002777804130000151
k (a) N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Pseudomonas aeruginosa 968584 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Acinetobacter baumannii 868485 Teicoplanin Sensitivity of
S9, dividing the susceptibility test record K (a) Y into the culture results of Escherichia coli or Klebsiella pneumoniae, and dividing the susceptibility test record K (c1) Y of susceptibility drugs in the range of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime and ceftazidime;
in one embodiment, referring to fig. 4, the step S9 may include steps S91-S92.
S91, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b1) Y with the culture result of Escherichia coli and Klebsiella pneumoniae and a drug sensitivity test record K (b1) N with the culture result of not Escherichia coli and Klebsiella pneumoniae based on the culture result;
specifically, the invention screens the drug sensitivity test records K (a) Y based on the field of 'culture result'. If the field of the 'culture result' is the escherichia coli and the klebsiella pneumoniae, the record K (b1) _ Y belongs to the susceptibility test record of the escherichia coli and the klebsiella pneumoniae as the culture result, otherwise, the record K (b1) _ N belongs to the drug susceptibility test record of the escherichia coli and the klebsiella pneumoniae as the culture result.
For the above-mentioned K (a) _ Y, K (b1) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
K (b1) _ N is:
Figure BDA0002777804130000152
Figure BDA0002777804130000161
s92, based on the susceptibility drug, assigning the susceptibility test record K (b1) _ Y to susceptibility test records K (c1) _ Y of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime, and performing susceptibility test records K (c1) _ N of other susceptibility drugs;
specifically, the invention screens the drug susceptibility test record K (b1) _ Y based on the drug susceptibility drug field in the drug susceptibility test record. A ' susceptibility test record K (c1) _ Y is assigned when the ' susceptibility drug ' field includes any of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime, or otherwise, K (c1) _ N.
For the above K (b1) _ Y, K (c1) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
K (c1) _ N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
S10, dividing the drug sensitive test record K (a) Y into a drug sensitive test record K (c2) Y with a culture result of enterococcus faecalis or enterococcus faecium and a drug sensitive drug of vancomycin;
in one embodiment, referring to fig. 5, the step S10 may include steps S101 to S102.
S101, based on the culture result, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b2) Y with the culture result of enterococcus faecalis and enterococcus faecium and a drug sensitivity test record K (b2) N with the culture result of not enterococcus faecalis and enterococcus faecium;
specifically, the invention screens the drug sensitivity test records K (a) Y based on the field of 'culture result'. And if the 'culture result' field is enterococcus faecalis and enterococcus faecium, the record K (b2) _ Y belongs to the drug sensitivity test record of the enterococcus faecalis and the enterococcus faecium with the culture result, otherwise, the record K (b2) _ N belongs to the drug sensitivity test record of the enterococcus faecalis and the enterococcus faecium with the culture result.
For the above-mentioned K (a) _ Y, K (b2) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Cefuroxime Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Vancomycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Penicillin Drug resistance
K (b2) _ N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefoxitin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Penicillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Vancomycin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Oxacillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Tetracycline derivatives Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Erythromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Rifampicin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Azithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clindamycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Compound sulfamethoxazole Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clarithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Latamoxef Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Moxifloxacin hydrate Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Minocycline Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Ciprofloxacin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Meropenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Golden YellowStaphylococcus chromogenes 968584 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Teicoplanin Sensitivity of
S102, dividing the drug susceptibility test record K (b2) _ Y into a drug susceptibility test record K (c2) _ Y of vancomycin and performing a drug susceptibility test record K (c2) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;
specifically, the invention screens the drug susceptibility test record K (b2) _ Y based on the drug susceptibility drug field in the drug susceptibility test record. When the 'drug susceptibility drug' field is vancomycin, the 'drug susceptibility drug' belongs to the drug susceptibility test record K (c2) _ Y, otherwise, the 'drug susceptibility drug' belongs to the drug susceptibility test record K (c2) _ N.
For the above K (b2) _ Y, K (c2) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Vancomycin Drug resistance
K (c2) _ N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Cefuroxime Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Penicillin Drug resistance
S11, dividing the drug sensitive test record K (a) Y into culture results of Acinetobacter baumannii or Pseudomonas aeruginosa, and dividing the drug sensitive test record K (c3) Y of drug sensitive drugs in the range of imipenem, meropenem and doripenem;
in one embodiment, referring to fig. 6, the step S11 may include steps S111-S112.
S111, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b3) Y with a culture result of Acinetobacter baumannii or Pseudomonas aeruginosa and a drug sensitivity test record K (b3) N with a culture result of Acinetobacter baumannii or Pseudomonas aeruginosa based on the culture result;
specifically, the invention screens the drug sensitivity test records K (a) Y based on the field of 'culture result'. And if the 'culture result' field is Acinetobacter baumannii and Pseudomonas aeruginosa, the drug sensitivity test record K (b3) _ Y belongs to the culture result of Acinetobacter baumannii and Pseudomonas aeruginosa, otherwise, the drug sensitivity test record K (b3) _ N belongs to the culture result of Acinetobacter baumannii and Pseudomonas aeruginosa.
For the above-mentioned K (a) _ Y, K (b3) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
K (b3) _ N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefoxitin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Penicillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Vancomycin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Oxacillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Tetracycline derivatives Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Erythromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Rifampicin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Azithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clindamycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Compound sulfamethoxazole Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clarithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Latamoxef Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Moxifloxacin hydrate Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Minocycline Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Ciprofloxacin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Meropenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Cefuroxime Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Vancomycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Penicillin Drug resistance
S112, based on the drug sensitive drugs, dividing the drug sensitive test records K (b3) _ Y into the drug sensitive test records K (c3) _ Y of the drug sensitive drugs in the ranges of imipenem, meropenem and doripenem, and carrying out the drug sensitive test records K (c3) _ N of other drug sensitive drugs;
specifically, the invention screens the drug susceptibility test record K (b1) _ Y based on the drug susceptibility drug field in the drug susceptibility test record. When the drug susceptibility drug field comprises any one of imipenem, meropenem and doripenem, the drug susceptibility drug field belongs to the drug susceptibility test record K (c3) _ Y, otherwise, the drug susceptibility test record K (c3) _ N.
For the above K (b3) _ Y, K (c3) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
K (c3) _ N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
S12, dividing the drug sensitive test record K (a) Y into a culture result of staphylococcus aureus and a drug sensitive test record K (c4) Y of drug sensitive drugs in the range of cefoxitin, oxacillin and methicillin;
in one embodiment, referring to fig. 7, the step S12 may include steps S121 to S122.
S121, dividing the susceptibility test record K (a) Y into a susceptibility test record K (b4) Y with a culture result of Staphylococcus aureus and a susceptibility test record K (b4) N with a culture result of not Staphylococcus aureus based on the culture result;
specifically, the invention screens the drug sensitivity test records K (a) Y based on the field of 'culture result'. When the "culture result" field is Staphylococcus aureus, it belongs to the susceptibility test record K (b4) _ Y for Staphylococcus aureus as the culture result, otherwise it belongs to the susceptibility test record K (b4) _ N for Staphylococcus aureus as the culture result.
For the above-mentioned K (a) _ Y, K (b4) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefoxitin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Penicillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Vancomycin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Oxacillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Tetracycline derivatives Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Erythromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Rifampicin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Azithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clindamycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Compound sulfamethoxazole Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Clarithromycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Latamoxef Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Moxifloxacin hydrate Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Minocycline Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Ciprofloxacin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Meropenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Imipenem Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefazolin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefuroxime Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Golden yellow colourStaphylococcus aureus 968584 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Teicoplanin Sensitivity of
K (b4) _ N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Teicoplanin Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Gentamicin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Cefuroxime Sensitivity of
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Vancomycin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Penicillin Drug resistance
S122, based on the drug sensitive drugs, dividing the drug sensitive test record K (b4) _ Y into the drug sensitive test records K (c4) _ Y of the drug sensitive drugs in the ranges of the Sphaestin, the oxacillin and the methicillin, and carrying out the drug sensitive test records K (c4) _ N of other drug sensitive drugs;
specifically, the invention screens the drug susceptibility test record K (b4) _ Y based on the drug susceptibility drug field in the drug susceptibility test record. When the drug susceptibility drug field comprises any one of Sporidin, oxacillin and methicillin, the drug susceptibility drug field belongs to the drug susceptibility test record K (c4) _ Y, otherwise, the drug susceptibility test record K (c4) _ N.
For the above K (b4) _ Y, K (c4) _ Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefoxitin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Oxacillin Drug resistance
J (c) and N are:
Figure BDA0002777804130000191
Figure BDA0002777804130000201
s13, merging the K (c1) Y, K (c2) Y, K (c3) Y, K (c4) Y to obtain a drug sensitivity test record K (d), and dividing the drug sensitivity test record K (d) into a drug sensitivity test record K (e) with a drug sensitivity result of intermediary or drug resistance and a drug sensitivity test record K (e) with a drug sensitivity result of no intermediary or drug resistance, wherein the drug sensitivity test record K (d) is divided into a drug sensitivity test record K (e) with a drug sensitivity result of intermediary or drug resistance and a drug sensitivity test record K (e) N;
the invention detects various multi-drug-resistant bacteria and respectively obtains drug sensitivity test records corresponding to the different multi-drug-resistant bacteria, thereby obtaining all the drug sensitivity test records of the multi-drug-resistant bacteria. Therefore, the invention combines the obtained drug susceptibility test results K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain the drug susceptibility test record K (d). This step is to combine the results of the different types of the detected multiple drug-resistant bacteria.
The invention screens the drug susceptibility test records K (d) based on the drug susceptibility result field in the drug susceptibility test records. When the drug sensitivity result field comprises any one of the intermediate or drug resistance, the drug sensitivity test record belongs to the record of detecting multiple drug resistance in the hospitalized patients, belongs to the drug sensitivity test record K (e) Y, and otherwise belongs to the drug sensitivity test record K (e) N.
For the above-mentioned K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y, K (d) is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefoxitin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Oxacillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Vancomycin Drug resistance
K (e) Y is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Cefoxitin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Staphylococcus aureus 968584 Oxacillin Drug resistance
123456(1) ICU 2019-01-05 10:17:00 Enterococcus faecium 584995 Vancomycin Drug resistance
K (e) N is:
patient's case number Inspection department Sampling time Results of the culture Sample number Medicine for treating drug allergy The result of drug sensitivity
And S14, outputting the times of the detection of multiple drug resistance cases in the hospitalized patients based on the number recorded in the drug susceptibility test record K (e) Y.
Specifically, the acquired susceptibility test record k (e) _ Y is record information related to the patient with the detected multiple drug resistance in the hospitalized patients. And outputting 0 if the drug susceptibility test record in the drug susceptibility test record K (e) Y is null, and outputting the number of the drug susceptibility test records K (e) Y if the drug susceptibility test record is not null, wherein the number of the cases is 0 as the number of the cases of detecting the multiple drug resistance in the inpatients.
For example, for k (e) _ Y mentioned above, since k (e) _ Y includes three records, the number of times of the detected multiple drug resistance cases in the hospitalized patient is output as 3.
Fig. 8 is a schematic block diagram of an apparatus for synchronously detecting the number of multiple drug resistance cases based on MapReduce and big data statistics, according to an embodiment of the present invention. As shown in fig. 8, the present invention also provides a device for counting the number of times of detecting multiple drug resistant cases based on MapReduce and big data, corresponding to the above statistical method for detecting the number of times of detecting multiple drug resistant cases in hospitalized patients. The device for counting the number of times of detection of multiple drug resistant cases in an inpatient includes a unit for performing the above-described counting method for the number of times of detection of multiple drug resistant cases in an inpatient, and the device may be configured in a server. Specifically, referring to fig. 8, the statistical apparatus for detecting the number of times of multiple drug resistance cases in a hospitalized patient includes a receiving unit, an acquiring and judging unit, a first parameter obtaining unit, a first division unit for a bacterial culture record, a fifth division unit for a bacterial culture record, a second parameter obtaining unit, a first division unit for a drug sensitivity test record, a fourth division unit for a drug sensitivity test record, a seventh division unit for a drug sensitivity test record, a tenth division unit for a drug sensitivity test record, a merging unit, and an output unit.
The receiving unit is used for receiving the statistical time and department selected by the user and determining the authority department of the user according to the identity information of the user; the collection and judgment unit is used for collecting the patient's branch information B, judging whether branch records of time and statistical time are crossed and departments simultaneously belong to the authority department and the selected department exist in the branch information B, calling the first parameter acquisition unit if the branch records exist, and outputting the number of times of detecting multiple drug resistance cases in the inpatient to be 0 if the branch records do not exist; the system comprises a first parameter acquisition unit, a second parameter acquisition unit and a third parameter acquisition unit, wherein the first parameter acquisition unit is used for acquiring hospitalization process information A of a patient, and acquiring the hospitalization time and the discharge time of the patient as a parameter g.MC2 together based on the hospitalization process information; a bacterial culture record first dividing unit for acquiring a bacterial culture record J, dividing the bacterial culture record J into bacterial culture records J (a) Y to be submitted during hospitalization of the patient and bacterial culture records J (a) N not to be submitted during hospitalization of the patient based on the parameter g.mc2; the bacteria culture record dividing unit is used for acquiring the bacteria culture records J (d) Y meeting the statistical time, the authority department and the selection limit in the bacteria culture records J (a) Y; a bacteria culture record fifth dividing unit for dividing the bacteria culture record j (d) Y into a bacteria culture record j (e) Y whose infection type is not contaminated and a bacteria culture record j (e) N whose infection type is contaminated; the second parameter acquisition unit is used for acquiring a test number list parameter g.MRO based on the bacterial culture record J (e) _ Y; the drug susceptibility test record first dividing unit is used for collecting a drug susceptibility test record K, and dividing the drug susceptibility test record K into a drug susceptibility test record K (a) Y in a specified test number range and a drug susceptibility test record K (a) N out of the specified test number range based on a parameter g.MRO; a susceptibility test record dividing unit for dividing the susceptibility test record K (a) Y into susceptibility test records K (c1) Y with a culture result of Escherichia coli or Klebsiella pneumoniae in the range of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime; the fourth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) _ Y into the drug susceptibility test record K (c2) _ Y of which the culture result is enterococcus faecalis or enterococcus faecium and the drug susceptibility drug is vancomycin; a seventh division unit for dividing the drug susceptibility test record K (a) _ Y into the culture results of Acinetobacter baumannii or Pseudomonas aeruginosa, and the drug susceptibility test records K (c3) _ Y of drug susceptibility drugs in the ranges of imipenem, meropenem and doripenem; the tenth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) _ Y into the drug susceptibility test record K (c4) _ Y with the culture result of staphylococcus aureus and the drug susceptibility drug in the ranges of Sphaestin, oxacillin and methicillin; a merging unit, configured to merge the K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain a drug sensitivity test record K (d), and divide the drug sensitivity test record K (d) into a drug sensitivity test record K (e) with a drug sensitivity result of intermediate or drug resistance and a drug sensitivity test record K (e) with a drug sensitivity result of non-intermediate or drug resistance based on the drug sensitivity result; and the output unit is used for outputting the times of the detected multiple drug resistance cases in the hospitalized patients based on the number recorded in the drug susceptibility test records K (e) Y.
In one embodiment, the collecting and judging unit comprises a first branch information dividing unit, a second branch information dividing unit, a third branch information dividing unit and a judging unit.
The first division unit of the information of the branch department, is used for gathering the information B of the branch department of the patient, divide the information B of the branch department into the information B (a) Y of the branch department that there is intersection with the said statistical time of time and information B (a) N of the branch department that the time does not intersect with the said statistical time.
A second division unit of branch information, configured to divide the branch information b (a) _ Y into branch information b (b) _ Y whose department belongs to the authority department and branch information b (b) _ N whose department does not belong to the authority department, based on the authority department.
A third division unit of the department information, which is used for dividing the department information B (b) _ Y into the department information B (c) _ Y of which the department belongs to the selected department and the department information B (c) _ N of which the department does not belong to the selected department based on the selected department.
A judging unit for judging whether the branch information B (c) Y has branch records, if yes, calling the first parameter obtaining unit, if not, outputting the detected multiple drug resistance example frequency of 0 in the hospitalized patient
In one embodiment, the bacteria culture record dividing unit comprises a second bacteria culture record dividing unit, a third bacteria culture record dividing unit and a fourth bacteria culture record dividing unit.
And a second division unit for dividing the bacteria culture record J (a) Y into bacteria culture records J (b) Y for censoring at the statistical time and bacteria culture records J (b) N not within the statistical time range.
And a third division unit for dividing the bacteria culture record J (b) _ Y into a checked-out bacteria culture record J (c) _ Y in the user authority department range and a checked-out bacteria culture record J (c) _ N out of the user authority department range based on the authority department.
And a bacteria culture record fourth dividing unit for dividing the bacteria culture record J (c) _ Y into a bacteria culture record J (d) _ Y for censorship in the censorship department selected by the user and a bacteria culture record J (c) _ N for censorship in a range not in the censorship department based on the selected department.
In one embodiment, the drug susceptibility test record dividing unit comprises a second drug susceptibility test record dividing unit and a third drug susceptibility test record dividing unit.
And the second division unit is used for dividing the drug susceptibility test record K (a) _ Y into a drug susceptibility test record K (b1) _ Y with the culture result of the Escherichia coli and the Klebsiella pneumoniae and a drug susceptibility test record K (b1) _ N with the culture result of the Escherichia coli and the Klebsiella pneumoniae.
A susceptibility test record third partition unit for partitioning the susceptibility test record K (b1) _ Y into susceptibility test records K (c1) _ Y for imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime, and performing susceptibility test records K (c1) _ N for other susceptibility drugs, based on the susceptibility drug.
In one embodiment, the fourth dividing unit for the drug susceptibility test record comprises a fifth dividing unit for the drug susceptibility test record and a sixth dividing unit for the drug susceptibility test record.
And the drug susceptibility test record fifth dividing unit is used for dividing the drug susceptibility test record K (a) _ Y into a drug susceptibility test record K (b2) _ Y of which the culture result is enterococcus faecalis and enterococcus faecium and a drug susceptibility test record K (b2) _ N of which the culture result is not enterococcus faecalis and enterococcus faecium based on the culture result.
And the drug susceptibility test record sixth dividing unit is used for dividing the drug susceptibility test record K (b2) _ Y into a drug susceptibility test record K (c2) _ Y of vancomycin and a drug susceptibility test record K (c2) _ N of other drug susceptibility drugs based on the drug susceptibility drugs.
In one embodiment, the seventh division unit of the drug susceptibility test record comprises an eighth division unit of the drug susceptibility test record and a ninth division unit of the drug susceptibility test record.
And the eighth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) Y into a drug susceptibility test record K (b3) Y with the culture result of Acinetobacter baumannii or Pseudomonas aeruginosa and a drug susceptibility test record K (b3) N with the culture result of Acinetobacter baumannii or Pseudomonas aeruginosa based on the culture result.
The ninth division unit for dividing the drug susceptibility test record K (b3) _ Y into the drug susceptibility test records K (c3) _ Y of the drug susceptibility drugs in the ranges of imipenem, meropenem and doripenem and carrying out the drug susceptibility test records K (c3) _ N of other drug susceptibility drugs based on the drug susceptibility drugs.
In one embodiment, the tenth division unit of the drug susceptibility test record comprises an eleventh division unit of the drug susceptibility test record and a twelfth division unit of the drug susceptibility test record.
A susceptibility test record eleventh dividing unit for dividing the susceptibility test record K (a) _ Y into a susceptibility test record K (b4) _ Y whose culture result is staphylococcus aureus and a susceptibility test record K (b4) _ N whose culture result is not staphylococcus aureus, based on the culture result.
And the drug susceptibility test record twelfth dividing unit is used for dividing the drug susceptibility test record K (b4) _ Y into the drug susceptibility test record K (c4) _ Y of the drug susceptibility drug in the ranges of the Sphaestin, the oxacillin and the methicillin and carrying out the drug susceptibility test record K (c4) _ N of other drug susceptibility drugs on the basis of the drug susceptibility drugs.
It should be noted that, as can be clearly understood by those skilled in the art, the above-mentioned apparatus for synchronously detecting the times of multiple drug resistance cases based on MapReduce and big data statistics and the specific implementation process of each unit may refer to the corresponding description in the foregoing method embodiments, and are not described herein again for convenience and brevity of description.
The device for synchronously detecting the times of the multiple drug resistance cases based on MapReduce and big data statistics can be implemented in the form of a computer program, and the computer program can be run on a computer device.
The computer device may be a server, wherein the server may be an independent server or a server cluster composed of a plurality of servers.
The computer device includes a processor, a memory, and a network interface connected by a system bus, where the memory may include a non-volatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform a method for contemporaneously detecting a number of multiple drug resistance cases based on MapReduce and big data statistics.
The processor is used to provide computational and control capabilities to support the operation of the overall computer device.
The internal memory provides an environment for running a computer program in a nonvolatile storage medium, and the computer program, when executed by a processor, can cause the processor to execute a method for detecting the number of the multiple drug resistance cases based on MapReduce and big data statistics.
The network interface is used for network communication with other devices. Those skilled in the art will appreciate that the above-described computer device configurations are merely part of the configurations associated with the present application and do not constitute a limitation on the computer devices to which the present application may be applied, and that a particular computer device may include more or less components than those shown in the figures, or may combine certain components, or have a different arrangement of components.
The processor is configured to run a computer program stored in a memory, and the program implements a method for detecting the number of times of the multiple drug resistance cases in the same period based on MapReduce and big data statistics in the first embodiment.
It should be understood that in the embodiments of the present Application, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
The invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program, when executed by a processor, causes the processor to perform a method for detecting the number of the multiple drug resistance cases simultaneously based on MapReduce and big data statistics according to an embodiment.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A method for synchronously detecting the times of multiple drug resistant cases based on MapReduce and big data statistics is characterized by comprising the following steps:
s1, receiving the statistical time and department selected by the user, and determining the authority department of the user according to the identity information of the user;
s2, collecting patient' S information B of transferring department, judging whether there is transferring record of time crossing with the statistical time and department belonging to the authority department and the selected department, if yes, executing step S3, if not, outputting the number of times of detecting multiple drug resistant cases in the inpatient as 0;
s3, acquiring hospitalization process information A of the patient, and acquiring the hospitalization time and the discharge time of the patient based on the hospitalization process information, wherein the hospitalization time and the discharge time are jointly used as parameters g.MC2;
s4, collecting a bacterial culture record J, dividing the bacterial culture record J into bacterial culture records J (a) _ Y submitted during patient hospitalization and J (a) _ N not submitted during patient hospitalization based on the parameter g.mc2;
s5, obtaining a bacterial culture record J (d) Y meeting the statistical time, the authority department and the selection limit from the bacterial culture records J (a) Y;
s6, dividing the bacterial culture record J (d) Y into a bacterial culture record J (e) Y with an infection type not polluted and a bacterial culture record J (e) N with an infection type polluted;
s7, acquiring a test number list parameter g.MRO based on the bacterial culture record J (e) Y;
s8, collecting a drug sensitivity test record K, and dividing the drug sensitivity test record K into a drug sensitivity test record K (a) Y in a specified test number range and a drug sensitivity test record K (a) N not in the specified test number range based on a parameter g.MRO;
s9, dividing the susceptibility test record K (a) Y into the culture results of Escherichia coli or Klebsiella pneumoniae, and dividing the susceptibility test record K (c1) Y of susceptibility drugs in the range of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime and ceftazidime;
s10, dividing the drug sensitive test record K (a) Y into a drug sensitive test record K (c2) Y with a culture result of enterococcus faecalis or enterococcus faecium and a drug sensitive drug of vancomycin;
s11, dividing the drug sensitive test record K (a) Y into culture results of Acinetobacter baumannii or Pseudomonas aeruginosa, and dividing the drug sensitive test record K (c3) Y of drug sensitive drugs in the range of imipenem, meropenem and doripenem;
s12, dividing the drug sensitive test record K (a) Y into a culture result of staphylococcus aureus and a drug sensitive test record K (c4) Y of drug sensitive drugs in the range of cefoxitin, oxacillin and methicillin;
s13, merging the K (c1) Y, K (c2) Y, K (c3) Y, K (c4) Y to obtain a drug sensitivity test record K (d), and dividing the drug sensitivity test record K (d) into a drug sensitivity test record K (e) with a drug sensitivity result of intermediary or drug resistance and a drug sensitivity test record K (e) with a drug sensitivity result of no intermediary or drug resistance, wherein the drug sensitivity test record K (d) is divided into a drug sensitivity test record K (e) with a drug sensitivity result of intermediary or drug resistance and a drug sensitivity test record K (e) N;
and S14, outputting the times of the detection of multiple drug resistance cases in the hospitalized patients based on the number recorded in the drug susceptibility test record K (e) Y.
2. The statistical method of claim 1, wherein the referral information includes patient case number, department, time of entry, time of exit; the hospitalization process information comprises a patient case number, an admission department, admission time, a discharge department and discharge time; the bacterial culture record comprises a patient case number, a submission department, a project name, sampling time, report time, a culture result, a sample number and a type; the drug sensitivity test records comprise patient case numbers, inspection departments, sampling time, culture results, sample numbers, drug sensitivity medicines and drug sensitivity results.
3. The statistical method according to claim 2, wherein the step S2 includes:
s21, collecting the patient' S information B of the branch department, dividing the information B of the branch department into information B (a) and Y of the branch department whose time is crossed with the statistical time and information B (a) and N of the branch department whose time is not crossed with the statistical time;
s22, dividing the branch information B (a) _ Y into branch information B (b) _ Y of which the department belongs to the authority department and branch information B (b) _ N of which the department does not belong to the authority department based on the authority department;
s23, dividing the branch information B (b) _ Y into branch information B (c) _ Y of which the department belongs to the selected department and branch information B (c) _ N of which the department does not belong to the selected department based on the selected department;
s24, judging whether the branch information B (c) and Y has branch records, if yes, executing step S3, and if not, outputting the number of times of detecting multiple drug resistance cases in the hospitalized patient as 0.
4. The statistical method according to claim 2, wherein the step S5 includes:
s51, dividing the bacterial culture record J (a) Y into a bacterial culture record J (b) Y which is checked at the statistical time and a bacterial culture record J (b) N which is not in the statistical time range;
s52, dividing the bacteria culture record J (b) Y into a checked bacteria culture record J (c) Y in the user authority department range and a checked bacteria culture record J (c) N out of the user authority department range based on the authority department;
s53, based on the selected department, dividing the bacterial culture record J (c) _ Y into the bacterial culture record J (d) _ Y for the delivery in the delivery department selected by the user and the bacterial culture record J (d) _ N for the delivery not in the delivery department.
5. The statistical method according to claim 2, wherein the step S9 includes:
s91, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b1) Y with the culture result of Escherichia coli and Klebsiella pneumoniae and a drug sensitivity test record K (b1) N with the culture result of not Escherichia coli and Klebsiella pneumoniae based on the culture result;
s92, based on the susceptibility drug, assigning the susceptibility test record K (b1) _ Y to susceptibility test records K (c1) _ Y of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime, and performing susceptibility test records K (c1) _ N of other susceptibility drugs;
the step S10 includes:
s101, based on the culture result, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b2) Y with the culture result of enterococcus faecalis and enterococcus faecium and a drug sensitivity test record K (b2) N with the culture result of not enterococcus faecalis and enterococcus faecium;
s102, dividing the drug susceptibility test record K (b2) _ Y into a drug susceptibility test record K (c2) _ Y of vancomycin and performing a drug susceptibility test record K (c2) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;
the step S11 includes:
s111, dividing the drug sensitivity test record K (a) Y into a drug sensitivity test record K (b3) Y with a culture result of Acinetobacter baumannii or Pseudomonas aeruginosa and a drug sensitivity test record K (b3) N with a culture result of Acinetobacter baumannii or Pseudomonas aeruginosa based on the culture result;
s112, based on the drug sensitive drugs, dividing the drug sensitive test records K (b3) _ Y into the drug sensitive test records K (c3) _ Y of the drug sensitive drugs in the ranges of imipenem, meropenem and doripenem, and carrying out the drug sensitive test records K (c3) _ N of other drug sensitive drugs;
the step S12 includes:
s121, dividing the susceptibility test record K (a) Y into a susceptibility test record K (b4) Y with a culture result of Staphylococcus aureus and a susceptibility test record K (b4) N with a culture result of not Staphylococcus aureus based on the culture result;
s122, based on the drug sensitive drugs, dividing the drug sensitive test record K (b4) _ Y into the drug sensitive test records K (c4) _ Y of the drug sensitive drugs in the ranges of the Sphaestin, the oxacillin and the methicillin, and carrying out the drug sensitive test records K (c4) _ N of other drug sensitive drugs.
6. A device for synchronously detecting the times of multiple drug resistant cases based on MapReduce and big data statistics is characterized by comprising the following steps:
the receiving unit is used for receiving the statistical time and department selected by the user and determining the authority department of the user according to the identity information of the user;
the collection and judgment unit is used for collecting the patient's branch information B, judging whether branch records of time and statistical time are crossed and departments simultaneously belong to the authority department and the selected department exist in the branch information B, calling the first parameter acquisition unit if the branch records exist, and outputting the number of times of detecting multiple drug resistance cases in the inpatient to be 0 if the branch records do not exist;
the system comprises a first parameter acquisition unit, a second parameter acquisition unit and a third parameter acquisition unit, wherein the first parameter acquisition unit is used for acquiring hospitalization process information A of a patient, and acquiring the hospitalization time and the discharge time of the patient as a parameter g.MC2 together based on the hospitalization process information;
a bacterial culture record first dividing unit for acquiring a bacterial culture record J, dividing the bacterial culture record J into bacterial culture records J (a) Y to be submitted during hospitalization of the patient and bacterial culture records J (a) N not to be submitted during hospitalization of the patient based on the parameter g.mc2;
the bacteria culture record dividing unit is used for acquiring the bacteria culture records J (d) Y meeting the statistical time, the authority department and the selection limit in the bacteria culture records J (a) Y;
a bacteria culture record fifth dividing unit for dividing the bacteria culture record j (d) Y into a bacteria culture record j (e) Y whose infection type is not contaminated and a bacteria culture record j (e) N whose infection type is contaminated;
the second parameter acquisition unit is used for acquiring a test number list parameter g.MRO based on the bacterial culture record J (e) _ Y;
the drug susceptibility test record first dividing unit is used for collecting a drug susceptibility test record K, and dividing the drug susceptibility test record K into a drug susceptibility test record K (a) Y in a specified test number range and a drug susceptibility test record K (a) N out of the specified test number range based on a parameter g.MRO;
a susceptibility test record dividing unit for dividing the susceptibility test record K (a) Y into susceptibility test records K (c1) Y with a culture result of Escherichia coli or Klebsiella pneumoniae in the range of imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime;
the fourth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) _ Y into the drug susceptibility test record K (c2) _ Y of which the culture result is enterococcus faecalis or enterococcus faecium and the drug susceptibility drug is vancomycin;
a seventh division unit for dividing the drug susceptibility test record K (a) _ Y into the culture results of Acinetobacter baumannii or Pseudomonas aeruginosa, and the drug susceptibility test records K (c3) _ Y of drug susceptibility drugs in the ranges of imipenem, meropenem and doripenem;
the tenth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) _ Y into the drug susceptibility test record K (c4) _ Y with the culture result of staphylococcus aureus and the drug susceptibility drug in the ranges of Sphaestin, oxacillin and methicillin;
a merging unit, configured to merge the K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain a drug sensitivity test record K (d), and divide the drug sensitivity test record K (d) into a drug sensitivity test record K (e) with a drug sensitivity result of intermediate or drug resistance and a drug sensitivity test record K (e) with a drug sensitivity result of non-intermediate or drug resistance based on the drug sensitivity result;
and the output unit is used for outputting the times of the detected multiple drug resistance cases in the hospitalized patients based on the number recorded in the drug susceptibility test records K (e) Y.
7. The statistical apparatus of claim 6, wherein the collecting and determining unit comprises:
a first division unit of the branch information, which is used for collecting the branch information B of the patient, and dividing the branch information B into branch information B (a) Y with the time crossed with the statistical time and branch information B (a) N with the time not crossed with the statistical time;
a subject information second dividing unit configured to divide the subject information b (a) _ Y into subject information b (b) _ Y whose subject belongs to the authority subject room and subject information b (b) _ N whose subject does not belong to the authority subject room, based on the authority subject room;
a third division unit of the department information, which is used for dividing the department information B (b) _ Y into the department information B (c) _ Y of which the department belongs to the selected department and the department information B (c) _ N of which the department does not belong to the selected department based on the selected department;
a judging unit, which is used for judging whether the branch information B (c) _ Y has a branch record, if yes, the first parameter obtaining unit is called, and if not, the frequency of detecting multiple drug resistance cases in the hospitalized patient is output as 0;
the bacteria culture record dividing unit comprises:
a second division unit for dividing the bacteria culture record J (a) Y into bacteria culture records J (b) Y for censoring at a statistical time and bacteria culture records J (b) N not within the statistical time range;
a third division unit of bacteria culture records, which is used for dividing the bacteria culture records J (b) _ Y into bacteria culture records J (c) _ Y for censorship in the range of the user authority department and bacteria culture records J (c) _ N for censorship out of the range based on the authority department;
and a bacteria culture record fourth dividing unit for dividing the bacteria culture record J (c) _ Y into a bacteria culture record J (d) _ Y for censorship in the censorship department selected by the user and a bacteria culture record J (d) _ N for censorship in a range not in the censorship department based on the selected department.
8. The statistical device of claim 6, wherein the drug susceptibility test record dividing unit comprises:
a second division unit for dividing the susceptibility test record K (a) _ Y into a susceptibility test record K (b1) _ Y and a susceptibility test record K (b1) _ N, based on the culture result, the culture result being Escherichia coli and Klebsiella pneumoniae, the culture result being other than Escherichia coli and Klebsiella pneumoniae;
a susceptibility test record third dividing unit for dividing the susceptibility test record K (b1) _ Y into susceptibility test records K (c1) _ Y for imipenem, meropenem, ertapenem, doripenem, cefotaxime, ceftriaxone, ceftazidime, ceftizoxime, and performing susceptibility test records K (c1) _ N for other susceptibility drugs, based on the susceptibility drug;
the fourth dividing unit of the drug susceptibility test record comprises:
a fifth division unit for dividing the test record K (a) Y into test records K (b2) Y whose culture results are enterococcus faecalis and enterococcus faecium and test records K (b2) N whose culture results are not enterococcus faecalis and enterococcus faecium;
the drug susceptibility test record sixth dividing unit is used for dividing the drug susceptibility test record K (b2) _ Y into a drug susceptibility test record K (c2) _ Y of vancomycin and a drug susceptibility test record K (c2) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;
the seventh dividing unit of the drug susceptibility test record comprises:
the eighth division unit of the drug susceptibility test record is used for dividing the drug susceptibility test record K (a) Y into a drug susceptibility test record K (b3) Y with the culture result of Acinetobacter baumannii or Pseudomonas aeruginosa and a drug susceptibility test record K (b3) N with the culture result of Acinetobacter baumannii or Pseudomonas aeruginosa based on the culture result;
the ninth division unit for dividing the drug susceptibility test record K (b3) _ Y into the drug susceptibility test records K (c3) _ Y of the drug susceptibility drugs in the ranges of imipenem, meropenem and doripenem and carrying out the drug susceptibility test records K (c3) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;
the tenth division unit of the drug susceptibility test record comprises:
a susceptibility test record eleventh dividing unit for dividing the susceptibility test record K (a) _ Y into a susceptibility test record K (b4) _ Y whose culture result is staphylococcus aureus and a susceptibility test record K (b4) _ N whose culture result is not staphylococcus aureus, based on the culture result;
and the drug susceptibility test record twelfth dividing unit is used for dividing the drug susceptibility test record K (b4) _ Y into the drug susceptibility test record K (c4) _ Y of the drug susceptibility drug in the ranges of the Sphaestin, the oxacillin and the methicillin and carrying out the drug susceptibility test record K (c4) _ N of other drug susceptibility drugs on the basis of the drug susceptibility drugs.
9. A computer arrangement, characterized in that the arrangement comprises a memory having stored thereon a computer program and a processor implementing the method according to any of claims 1-5 when executing the computer program.
10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 5.
CN202011271392.7A 2020-11-13 2020-11-13 Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics Pending CN112542249A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011271392.7A CN112542249A (en) 2020-11-13 2020-11-13 Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011271392.7A CN112542249A (en) 2020-11-13 2020-11-13 Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics

Publications (1)

Publication Number Publication Date
CN112542249A true CN112542249A (en) 2021-03-23

Family

ID=75014458

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011271392.7A Pending CN112542249A (en) 2020-11-13 2020-11-13 Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics

Country Status (1)

Country Link
CN (1) CN112542249A (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1635150A (en) * 2004-10-21 2005-07-06 复旦大学附属华山医院 Medicine and bacterium resistant detection chip, method for preparation and application thereof
CN103310104A (en) * 2013-06-14 2013-09-18 浙江大学 Drug resistance monitoring system for bacteria
CN105950701A (en) * 2016-05-20 2016-09-21 深圳市南山区人民医院 Method and reagent for detecting resistant staphylococcus aureus colonization
CN109065178A (en) * 2018-07-11 2018-12-21 南京医科大学第二附属医院 A kind of nosocomial infection early warning construction method based on hospital information system
CN111243756A (en) * 2020-01-21 2020-06-05 杭州杏林信息科技有限公司 Method and device for counting infection cases of type I incision operation part and storage medium
CN111261304A (en) * 2020-01-21 2020-06-09 杭州杏林信息科技有限公司 Statistical method, device and storage medium for detecting number of methicillin-resistant staphylococcus aureus strains
CN111312404A (en) * 2020-01-21 2020-06-19 杭州杏林信息科技有限公司 Method, equipment and storage medium for counting number of blood stream infected persons related to new central vascular catheter
AU2020101023A4 (en) * 2020-06-16 2020-07-23 Guizhou Minzu University A Novel Deep Learning Approach for Distributed Traffic Flow Forecasting

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1635150A (en) * 2004-10-21 2005-07-06 复旦大学附属华山医院 Medicine and bacterium resistant detection chip, method for preparation and application thereof
CN103310104A (en) * 2013-06-14 2013-09-18 浙江大学 Drug resistance monitoring system for bacteria
CN105950701A (en) * 2016-05-20 2016-09-21 深圳市南山区人民医院 Method and reagent for detecting resistant staphylococcus aureus colonization
CN109065178A (en) * 2018-07-11 2018-12-21 南京医科大学第二附属医院 A kind of nosocomial infection early warning construction method based on hospital information system
CN111243756A (en) * 2020-01-21 2020-06-05 杭州杏林信息科技有限公司 Method and device for counting infection cases of type I incision operation part and storage medium
CN111261304A (en) * 2020-01-21 2020-06-09 杭州杏林信息科技有限公司 Statistical method, device and storage medium for detecting number of methicillin-resistant staphylococcus aureus strains
CN111312404A (en) * 2020-01-21 2020-06-19 杭州杏林信息科技有限公司 Method, equipment and storage medium for counting number of blood stream infected persons related to new central vascular catheter
AU2020101023A4 (en) * 2020-06-16 2020-07-23 Guizhou Minzu University A Novel Deep Learning Approach for Distributed Traffic Flow Forecasting

Similar Documents

Publication Publication Date Title
Donnelly et al. Antibiotic utilization for acute respiratory tract infections in US emergency departments
Kobayashi et al. Outpatient antibiotic prescribing practices for uncomplicated urinary tract infection in women in the United States, 2002–2011
Morales et al. Hospital costs of nosocomial multi-drug resistant Pseudomonas aeruginosa acquisition
Hayakawa et al. Comparison of the clinical characteristics and outcomes associated with vancomycin-resistant Enterococcus faecalis and vancomycin-resistant E. faecium bacteremia
Beaudoin et al. Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs
Hord et al. Central line associated blood stream infections in pediatric hematology/oncology patients with different types of central lines
CN111309781B (en) Method and equipment for counting number of pathogen censored persons before treatment by antibacterial drugs
Leal et al. Development of a novel electronic surveillance system for monitoring of bloodstream infections
CN111261304B (en) Statistical method, device and storage medium for detecting methicillin-resistant staphylococcus aureus strain number
Bouganim et al. The clinical and molecular epidemiology of noncarbapenemase-producing carbapenem-resistant Enterobacteriaceae: a case-case-control matched analysis
Arcenillas et al. Assessment of quality indicators for appropriate antibiotic use
CN114171148B (en) Method, device, equipment and storage medium for monitoring aggregated infection route
Qureshi et al. Safety and efficacy of long-term outpatient ertapenem therapy
Rhodes et al. Trends in and predictors of carbapenem consumption across North American hospitals: results from a multicenter survey by the MAD-ID research network
Tan et al. Laboratory antibiotic susceptibility reporting and antibiotic prescribing in general practice
Kärpänoja et al. Connection between trimethoprim-sulfamethoxazole use and resistance in Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis
Li et al. Economic burden attributable to healthcare-associated infections in tertiary public hospitals of Central China: a multi-centre case-control study
Jackson et al. Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies
Wang et al. A general propensity score for signal identification using tree-based scan statistics
CN112582042A (en) Method and system for synchronously detecting times of infected multi-drug-resistant bacteria cases based on MapReduce and big data management
CN112542249A (en) Method and device for synchronously detecting times of multiple drug resistance cases based on MapReduce and big data statistics
Veve et al. Comparison of neutropenia associated with ceftaroline or ceftriaxone in patients receiving at least 7 days of therapy for severe infections
Leal et al. The validation of a novel surveillance system for monitoring bloodstream infections in the Calgary zone
Bader et al. Time for precision: a world without susceptibility breakpoints
Crowe et al. Epidemiology of bloodstream infections in a Scottish haemodialysis population with focus on vascular access method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination