CN115910374A - Early warning method and medium for aggregation or outbreak time of hospital infectious diseases - Google Patents
Early warning method and medium for aggregation or outbreak time of hospital infectious diseases Download PDFInfo
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Abstract
The invention provides a hospital infectious disease aggregation or outbreak time early warning method and a medium, which belong to the technical field of medical information and specifically comprise the following steps: the method comprises the steps of obtaining infection data of a whole hospital, extracting the medication behavior of anti-infective drugs of doctors corresponding to suspected patients when the suspected patients with suspected infectious diseases exist, evaluating the severity of the suspected patients, obtaining pathogen detection results of the suspected patients when the severity of the suspected patients is larger than a first threshold, outputting infectious disease aggregation early warning signals and recording early warning time when the pathogen detection results judge that the number of the patients with the same pathogen detection results in the whole hospital is larger than a second threshold, wherein the second threshold is determined according to the scale of the hospital, the number of hospitalizations of the hospital and the daily average number of outpatients of the hospital, and therefore the accuracy and timeliness of judgment of the infectious disease aggregation or outbreak time are further improved.
Description
Technical Field
The invention belongs to the technical field of medical information, and particularly relates to a hospital infectious disease aggregation or outbreak time early warning method and medium.
Background
In order to realize early warning of the aggregated epidemic situation of the infection diseases at the hospital side, in the invention patent publication No. CN113506635A, a method and a device for capturing keywords of hospital infection data, hospital infection rules and a data source in a preset time period are obtained according to target requirements; target key words are obtained from the hospital infection rules, data to be analyzed are obtained from the data source, the captured key words can accurately judge whether a certain symptom occurs to a patient in a hospital or not in the hospital, and the captured key words can be used for judging whether a hospital infection early warning is generated or not by combining with other data of the patient, so that extraction errors are reduced, but the technical problem is solved:
1. the disease severity of the patient is evaluated without combining the medication behavior of the anti-infective drug of the doctor, and if the disease severity of the patient is low, the patient cannot cause serious infectious disease aggregation epidemic at the moment, so that the final time early warning accuracy is low.
2. The early warning time is not determined by combining the detection results of the same types of abnormal pathogens in the hospital, only when a plurality of abnormal pathogen detection results appear in the hospital, the aggregated epidemic situation of infectious diseases can appear, and if the factors are not considered, the problem that the judgment result is inaccurate due to the abnormal patient detection results of some externally input patients also exists.
3. The threshold value of the number of abnormal pathogen detection results of the same type is not set depending on the scale of the hospital, the number of hospitalizations, and the like, but the larger the scale of the hospital, the more the number of hospitalizations, the more serious the risk of outbreak of infectious diseases, and if the threshold value of the number cannot be corrected, the outbreak of explosive infectious diseases is inevitable.
Based on the above technical problems, there is a need to design and provide a method and medium for early warning of the time of aggregation or outbreak of nosocomial infectious diseases.
Disclosure of Invention
The invention aims to provide a method and a medium for early warning the aggregation or outbreak time of hospital infectious diseases.
In order to solve the technical problem, the invention provides a method for early warning the aggregation or outbreak time of a hospital infectious disease in a first aspect, which comprises the following steps:
s11, acquiring infection data of a whole hospital, preprocessing the infection data, and entering S12 when a suspected patient with suspected infectious diseases exists;
s12, extracting the medication behavior of the anti-infective drug of the doctor corresponding to the suspected patient, evaluating the severity of the suspected patient, and entering the step S13 when the severity of the suspected patient is greater than a first threshold;
s13, obtaining a pathogen detection result of the suspected patient, judging whether the number of the patients with the same pathogen detection result in the whole hospital is larger than a second threshold value or not based on the pathogen detection result, if so, entering a step S14, and if not, returning to the step S11, wherein the second threshold value is determined according to the scale of the hospital, the number of inpatients in the hospital and the number of daily outpatients and receptions of the hospital;
and S14, outputting an infectious disease aggregation early warning signal, and recording the early warning time at the moment.
The severity of the suspected patient is evaluated by combining the medication behavior of the anti-infective drug of the doctor, so that the severity of the suspected patient can be judged more accurately by combining the self condition of the suspected patient and hooking with the behavior of the doctor.
Through the setting of first threshold value to make infectious diseases gather early warning signal's output can get in touch with patient's serious degree, not only reduced unnecessary judgement, promoted the efficiency of judgement moreover by a wide margin.
The second threshold value is determined based on the scale of the hospital, the number of inpatients in the hospital and the number of daily outpatient services in the hospital, so that the threshold value judgment of the number of patients with the same pathogen detection result can be combined with the actual condition of the hospital, the output of infectious disease gathering early warning signals can be more accurately and rapidly realized, and unnecessary infection and diseases are further reduced.
The further technical scheme is that the infection data at least comprises any one of diagnosis data, inspection data, examination data and bacteria culture data of outpatient and inpatient areas of the whole hospital.
The further technical scheme is that the method for evaluating the severity of the suspected patient comprises the following specific steps:
s21, acquiring the types and the dosages of the anti-infective drugs prescribed by the doctor corresponding to the suspected patient, and entering the step S22 when the types of the anti-infective drugs are larger than a first quantity threshold value and the dosages of any type of anti-infective drugs are larger than a first dosage threshold value;
s22, judging whether the number of hospitalization days of the suspected patient is larger than a first day threshold value, if so, entering a step S23, otherwise, returning to the step S21;
s23, acquiring a pathogen detection result of the suspected patient, and obtaining the infection disease type of the suspected patient by adopting a classification model based on an SVM algorithm based on the pathogen detection result;
s24, the type and the dosage of the anti-infective drug are used as an input set, and the severity degree of the suspected patient is obtained based on a pre-trained infectious disease severity prediction model corresponding to the infectious disease type based on the SSA-SCR algorithm.
Through the setting of the first quantity threshold and the first dose threshold, unnecessary judgment is greatly reduced, the screening of suspected patients with certain severity is realized, the judgment efficiency is improved, and meanwhile, the accuracy of the evaluation of the severity of the suspected patients is ensured.
Through the judgment of the first day threshold value, the screening of suspected patients with more living days and certain severity is realized, and the technical problem of wrong judgment caused by the fact that the patients belong to the transfer patients or the living days are few can be reduced.
A further technical solution is that the first number threshold, the first dose threshold, and the first day threshold are determined according to the lesion where the suspected patient is located and the diagnosis result of the suspected patient, wherein the simpler the isolation measure is, the larger the number of patients and the more serious the diagnosis result of the suspected patient is, the smaller the first number threshold, the first dose threshold, and the first day threshold are.
The further technical scheme is that the scale of the hospital is determined according to the grade of the hospital, the number of sickbeds and the number of doctors.
The further technical scheme is that the specific steps of judging and outputting the infectious disease aggregation early warning signal are as follows:
s31, judging whether the number of patients with the same pathogen detection result in all the hospitals is larger than a second threshold value or not based on the pathogen detection result, and if so, entering the step S32;
s32, judging whether the number of the patients with the same pathogen detection result in the whole hospital is larger than a third threshold value or not based on the pathogen detection result, if so, entering step S34, otherwise, entering step S33, wherein the third threshold value is larger than the second threshold value;
s33, extracting the number of hospitalization days of the patients with the same pathogen detection result, taking the patients with the same pathogen detection result with the number of hospitalization days larger than a second number-of-days threshold value as potentially infected patients, entering the step S34 if and only if the number of the potentially infected patients is larger than a fourth threshold value and the number of the ward areas where the potentially infected patients are located is larger than a first ward area number threshold value, and otherwise, returning to the step S31;
s34, outputting an infectious disease aggregation early warning signal.
Through the setting of third threshold value to can realize faster infectious diseases gathering early warning signal's output, make under the more circumstances of infectious population, can reduce the propagation risk that infectious diseases led to, promote hospital's patient and medical personnel's security.
Through the setting of first ward quantity threshold value and fourth threshold value to can further combine latent infection patient's days of hospitalization and ward quantity, further guarantee infectious diseases gathering early warning signal output's reliability and accuracy, and promote holistic reliability.
The further technical scheme is that before the infectious disease aggregation early warning signal is output, hospitalization process information, referral information and infection information of the suspected infected patient need to be judged.
On the other hand, the invention provides a hospital infectious disease aggregation or outbreak time early warning device, which adopts the hospital infectious disease aggregation or outbreak time early warning method and comprises a suspected patient screening module, a severity degree evaluation module, a pathogen detection determination module and an early warning signal output module;
wherein the suspected patient screening module is responsible for screening suspected patients for the presence of suspected infectious diseases;
the severity evaluation module is responsible for evaluating the severity of suspected patients;
the pathogen detection determining module is responsible for acquiring a pathogen detection result of a suspected patient and judging whether the number of the patients with the same pathogen detection result in all the hospitals is larger than a second threshold value or not based on the pathogen detection result;
the early warning signal output module is responsible for outputting an infectious disease gathering early warning signal and recording the early warning time at the moment.
In another aspect, the present invention provides a terminal device, including a memory, a processor, and a hospital infectious disease aggregation or outbreak time early warning program stored in the memory and executable on the processor, where the processor implements the hospital infectious disease aggregation or outbreak time early warning method program to implement the hospital infectious disease aggregation or outbreak time early warning method.
In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute a method for early warning of the aggregation or outbreak time of a hospital infectious disease as described above.
Additional features and advantages will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
FIG. 1 is a flowchart of a method for warning the time to aggregation or outbreak of a nosocomial infectious disease according to example 1;
fig. 2 is a block diagram of a hospital infectious disease aggregation or outbreak time warning device in example 2.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their detailed description will be omitted.
The terms "a," "an," "the," "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.
Example 1
In order to solve the above problems, according to one aspect of the present invention, as shown in fig. 1, there is provided a method for early warning of the time of aggregation or outbreak of a nosocomial infectious disease, comprising:
s11, acquiring infection data of a whole hospital, preprocessing the infection data, and entering S12 when a suspected patient with suspected infectious diseases exists;
specifically, for example, infection data of a hospital information system is extracted to form metadata so as to obtain a metadata table; performing data cleaning and preprocessing on the metadata table, deleting repeated and non-infected cases, and then storing the cleaned and filtered data table into a newly-built aggregated event infection database; monitoring of infection data carries out centralized and important monitoring on diagnosis, inspection, examination, bacteria culture, medical record information and infection reporting cards of outpatient and inpatient areas in the whole hospital, and mainly focuses on the infection result of a patient.
For example, the infection data may be acquired by using a data middleware, which supports acquiring medical procedure data from a plurality of data sources in various forms of interface manners (such as a database, a message model, a webservice, an MQ, an excel table, http, a file, etc.), forming a specific unified data format through a unified data conversion component, and finally storing the data in an aggregated event infection database.
Specifically, for example, a suspected patient of a suspected infectious disease is screened by monitoring the infection result, diagnosis result, medication condition, and the like of the patient.
S12, extracting the medication behavior of the anti-infective drug of the doctor corresponding to the suspected patient, evaluating the severity of the suspected patient, and entering the step S13 when the severity of the suspected patient is greater than a first threshold;
in particular, for example, in anti-infective therapy, when a patient is infected in a hospital and the disease progresses beyond the control range of a doctor, the doctor can upgrade the anti-infective drug and check the etiology to obtain a targeted therapy basis. The abnormal diagnosis and treatment behaviors of the doctor mainly take the medicine upgrading condition of the inpatient as the main condition, and the severity degree of the abnormal patient is evaluated.
For example, the severity can range from 0 to 1, wherein the greater the severity, the greater the severity.
S13, obtaining a pathogen detection result of the suspected patient, judging whether the number of the patients with the same pathogen detection result in the whole hospital is larger than a second threshold value or not based on the pathogen detection result, if so, entering a step S14, and if not, returning to the step S11, wherein the second threshold value is determined according to the scale of the hospital, the number of inpatients in the hospital and the number of daily outpatients and receptions of the hospital;
for example, the number of patients with the same pathogen detection result in all the hospitals can be 10, and the second threshold is constructed by using a model based on a neural network algorithm.
And S14, outputting an infectious disease aggregation early warning signal, and recording the early warning time at the moment.
The severity of the suspected patient is evaluated by combining the medication behavior of the anti-infective drug of the doctor, so that the severity of the suspected patient can be more accurately judged by combining the self condition of the suspected patient and further hooking with the behavior of the doctor.
Through the setting of first threshold value to make infectious diseases gather early warning signal's output can get in touch with patient's serious degree, not only reduced unnecessary judgement, promoted the efficiency of judgement moreover by a wide margin.
The second threshold is determined based on the scale of the hospital, the number of inpatients in the hospital and the number of daily outpatient services in the hospital, so that the threshold judgment of the number of patients with the same pathogen detection result can be combined with the actual condition of the hospital, the output of infectious disease gathering early warning signals can be more accurately and rapidly realized, and unnecessary infection and disease are further reduced.
In another possible embodiment, the infection data includes at least any one of diagnosis data, test data, examination data, and bacteria culture data of all outpatients and inpatients areas of the hospital.
For example, the in-hospital pathogen submission and detection data of each department in last two years are used for submission and detection of the pathogens, the submission and detection quantity standards of different pathogens of each department every day, every 7 days and every 30 days are established through a time sequence algorithm based on machine learning, and a reasonable threshold value is provided by combining different types of pathogenic microorganisms of each disease area, so that the anomaly monitoring is realized.
In another possible embodiment, the evaluation of the severity of the suspected patient comprises the following specific steps:
s21, acquiring the types and the dosages of the anti-infective drugs prescribed by the doctor corresponding to the suspected patient, and entering S22 when the types of the anti-infective drugs are larger than a first quantity threshold value and the dosages of any type of anti-infective drugs are larger than a first dosage threshold value;
specifically, the anti-infective drug refers to various antibiotics, sulfa drugs and other chemical synthetic drugs (isoniazid, metronidazole, nitrofurantoin, pipemidic acid, etc.) which have the effect of killing or inhibiting various pathogenic microorganisms and can be applied to the whole body such as oral administration, intramuscular injection, intravenous injection, etc.
S22, judging whether the number of hospitalization days of the suspected patient is larger than a first day threshold value, if so, entering a step S23, otherwise, returning to the step S21;
s23, acquiring a pathogen detection result of the suspected patient, and obtaining the infection disease type of the suspected patient by adopting a classification model based on an SVM algorithm based on the pathogen detection result;
s24, the type and the dosage of the anti-infective drug are used as an input set, and the severity degree of the suspected patient is obtained based on a pre-trained infectious disease severity prediction model corresponding to the infectious disease type based on the SSA-SCR algorithm.
Specifically, for example, the SSA algorithm is used to optimize the initial value of the SCR algorithm.
For example, in order to make the variation range more uniform, an improved cauchy variation perturbation degree strategy is adopted to optimize the individual position update of the SSA algorithm, and the specific calculation formula is as follows:
in the formula: x is the original individual position, cauchy (x) is the individual position after Cauchy variation, u is a random number with the value range of 0 to 1, t is the current iteration number, t max To the maximum number of iterations, K 1 Is constant and takes a value between 0 and 1.
Through the setting of the first quantity threshold and the first dosage threshold, unnecessary judgment is greatly reduced, the screening of suspected patients with certain severity is realized, the judgment efficiency is improved, and meanwhile, the accuracy of the evaluation of the severity of the suspected patients is ensured.
Through the judgment of the first day threshold value, the screening of suspected patients with more living days and certain severity is realized, and the technical problem of wrong judgment caused by the fact that the patients belong to the transfer patients or the living days are few can be reduced.
In another possible embodiment, the first number threshold, the first dose threshold and the first day threshold are determined according to the lesion where the suspected patient is located and the diagnosis result of the suspected patient, wherein the simpler the isolation measure in the lesion where the suspected patient is located, the greater the number of patients and the more serious the diagnosis result of the suspected patient are, the smaller the first number threshold, the first dose threshold and the first day threshold are.
In another possible embodiment, the size of the hospital is determined according to the hospital level, the number of beds, and the number of doctors.
In another possible embodiment, the specific steps of determining and outputting the infectious disease aggregation warning signal are as follows:
s31, judging whether the number of patients with the same pathogen detection result in all the hospitals is larger than a second threshold value or not based on the pathogen detection result, and if so, entering the step S32;
s32, judging whether the number of the patients with the same pathogen detection result in the whole hospital is larger than a third threshold value or not based on the pathogen detection result, if so, entering step S34, otherwise, entering step S33, wherein the third threshold value is larger than the second threshold value;
specifically, for example, if the number of patients with the same pathogen detection result is 100 and the third threshold is 50, the process goes directly to step S34.
S33, extracting the number of hospitalization days of the patients with the same pathogen detection result, taking the patients with the same pathogen detection result with the number of hospitalization days larger than a second number-of-days threshold value as potentially infected patients, entering the step S34 if and only if the number of the potentially infected patients is larger than a fourth threshold value and the number of the ward areas where the potentially infected patients are located is larger than a first ward area number threshold value, and otherwise, returning to the step S31;
s34, outputting an infectious disease aggregation early warning signal.
Through the setting of third threshold value to can realize faster infectious diseases gathering early warning signal's output, make under the more condition of infectious population, can reduce the propagation risk that infectious diseases lead to, promote hospital's patient and medical personnel's security.
Through the setting of first ward quantity threshold value and fourth threshold value to can further combine latent infection patient's the number of days of stay and ward quantity, further guarantee infectious diseases gathering early warning signal output's reliability and accuracy, and promote holistic reliability.
In another possible embodiment, before outputting the early warning signal of infectious disease aggregation, the hospitalization information, the referral information, and the infection information of the suspected infected patient are determined.
For example, aggregation judgment is carried out according to possible propagation paths (propagation caused by nursing staff, propagation caused by ward and propagation caused by bed sharing before and after the abnormal patients are screened out), reasonable aggregation early warning conditions are set according to the infection conditions of the patients, and accurate early warning of hospital infection outbreak is achieved.
For example, the doctor gives an early warning of abnormal diagnosis and treatment behaviors, and after patients are grouped, the time-space relationship analysis is performed on the abnormal patients in the same group:
acquiring hospitalization process information of a patient, and acquiring admission time and discharge time of the patient based on the hospitalization process information, wherein the admission time and the discharge time are jointly used as parameters g.MC2;
acquiring patient transfer information B, acquiring patient bed distribution information C, and acquiring patient information A containing bed distribution by combining the information B;
acquiring nursing information R of medical staff, and obtaining patient information R (a) by combining patient information A containing bed distribution;
based on the fact that R (a) contains the information of time, departments, beds and nursing staff involved in the process from the hospitalization to the discharge of the patients, the patients are associated according to the space-time relationship to form a relationship network G among the patients.
Dividing the drug-resistant bacteria inspection information J into drug-resistant bacteria inspection information J (a) of which the infection type is HA and drug-resistant bacteria inspection information J (b) of which the infection type is not HA based on the parameter g.MC2;
based on the drug-resistant bacteria inspection information J (b), an infection relation network G (a) is obtained by combining the patient relation network G, and when the patients with the same type of multi-drug-resistant bacteria are detected to have a space-time relation, the infection relation network G (a) is possibly caused by mutual transmission;
at the moment, the relevant infected patients output early warning.
Example 2
As shown in fig. 2, the invention provides a hospital infectious disease aggregation or outbreak time early warning device, which comprises a suspected patient screening module, a severity evaluation module, a pathogen detection determination module and an early warning signal output module;
wherein the suspected patient screening module is responsible for screening suspected patients for the presence of suspected infectious diseases;
the severity evaluation module is responsible for evaluating the severity of suspected patients;
the pathogen detection determining module is responsible for acquiring a pathogen detection result of a suspected patient and judging whether the number of the patients with the same pathogen detection result in all the hospitals is larger than a second threshold value or not based on the pathogen detection result;
the early warning signal output module is responsible for outputting an infectious disease gathering early warning signal and recording the early warning time at the moment.
Example 3
The invention provides a terminal device which comprises a memory, a processor and a hospital infectious disease gathering or outbreak time early warning program, wherein the hospital infectious disease gathering or outbreak time early warning program is stored in the memory and can be operated on the processor, and when the processor executes the hospital infectious disease gathering or outbreak time early warning method program, the hospital infectious disease gathering or outbreak time early warning method is realized.
Example 4
The present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to execute a hospital infectious disease aggregation or outbreak time early warning method as described above.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The system embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may 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 server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the contents of the specification, and must be determined according to the scope of the claims.
Claims (10)
1. A hospital infectious disease aggregation or outbreak time early warning method is characterized by specifically comprising the following steps:
s11, acquiring infection data of a whole hospital, preprocessing the infection data, and entering S12 when a suspected patient with suspected infectious diseases exists;
s12, extracting the medication behavior of the anti-infective drug of the doctor corresponding to the suspected patient, evaluating the severity of the suspected patient, and entering the step S13 when the severity of the suspected patient is greater than a first threshold;
s13, obtaining a pathogen detection result of the suspected patient, judging whether the number of patients with the same pathogen detection result in all hospitals is larger than a second threshold value or not based on the pathogen detection result, if so, entering a step S14, otherwise, returning to the step S11, wherein the second threshold value is determined according to the scale of the hospital, the number of inpatients in the hospital and the number of daily outpatients and waiters of the hospital;
and S14, outputting an infectious disease aggregation early warning signal, and recording the early warning time at the moment.
2. The method for warning of the time to aggregation or outbreak of nosocomial infectious diseases according to claim 1, wherein said infection data comprises at least any one of diagnosis data, test data, examination data, and bacteria culture data of outpatient and inpatient areas of a hospital.
3. The method for warning the time to the aggregation or outbreak of a nosocomial infectious disease according to claim 1, wherein the step of evaluating the severity of the suspected patient comprises the steps of:
s21, acquiring the types and the dosages of the anti-infective drugs prescribed by the doctor corresponding to the suspected patient, and entering the step S22 when the types of the anti-infective drugs are larger than a first quantity threshold value and the dosages of any type of the anti-infective drugs are larger than a first dosage threshold value;
s22, judging whether the number of hospitalization days of the suspected patient is larger than a first day threshold value, if so, entering a step S23, otherwise, returning to the step S21;
s23, acquiring a pathogen detection result of the suspected patient, and obtaining the infection disease type of the suspected patient by adopting a classification model based on an SVM algorithm based on the pathogen detection result;
s24, the type and the dosage of the anti-infective drug are used as an input set, and the severity degree of the suspected patient is obtained based on a pre-trained infectious disease severity prediction model corresponding to the infectious disease type based on the SSA-SCR algorithm.
4. The method for warning the time to aggregate or outbreak of a hospital infectious disease according to claim 3, wherein the first number threshold, the first dose threshold and the first day threshold are determined according to the area of suspected patient and the diagnosis result of the suspected patient, wherein the simpler the isolation measure of the area of suspected patient is, the larger the number of patients and the more serious the diagnosis result of the suspected patient is, the smaller the first number threshold, the first dose threshold and the first day threshold are.
5. The method for warning of the time to aggregation or outbreak of an infectious disease in a hospital according to claim 1, wherein the size of said hospital is determined according to the grade of said hospital, the number of beds and the number of doctors.
6. The method for early warning of the aggregation or outbreak time of the nosocomial infectious diseases according to claim 1, wherein the specific steps of judging and outputting the early warning signal of the aggregation of the infectious diseases are as follows:
s31, judging whether the number of patients with the same pathogen detection result exists in all the hospitals is larger than a second threshold value or not based on the pathogen detection result, and if so, entering the step S32;
s32, judging whether the number of patients with the same pathogen detection result in all the hospitals is larger than a third threshold value or not based on the pathogen detection result, if so, entering a step S34, otherwise, entering a step S33, wherein the third threshold value is larger than the second threshold value;
s33, extracting the number of hospitalizations of the patients with the same pathogen detection result, taking the patients with the same pathogen detection result, the number of hospitalizations of which is greater than a second threshold of days, as potentially infected patients, and entering the step S34 if and only if the number of potentially infected patients is greater than a fourth threshold and the number of the cells in which the potentially infected patients are located is greater than a first threshold of cell number, otherwise, returning to the step S31;
s34, outputting an infectious disease aggregation early warning signal.
7. The method according to claim 1, wherein before outputting the early warning signal of infectious disease aggregation, the hospital stay information, the referral information, and the infection information of the suspected infected patient are determined.
8. A hospital infectious disease aggregation or outbreak time early warning device, which adopts the hospital infectious disease aggregation or outbreak time early warning method of any one of claims 1 to 7, and comprises a suspected patient screening module, a severity evaluation module, a pathogen detection determination module and an early warning signal output module;
wherein the suspected patient screening module is responsible for screening suspected patients for the presence of suspected infectious diseases;
the severity assessment module is responsible for assessing the severity of the suspected patient;
the pathogen detection determining module is responsible for acquiring a pathogen detection result of a suspected patient and judging whether the number of the patients with the same pathogen detection result in all the hospitals is larger than a second threshold value or not based on the pathogen detection result;
the early warning signal output module is responsible for outputting an infectious disease gathering early warning signal and recording the early warning time at the moment.
9. A terminal device, comprising a memory, a processor and a hospital infectious disease aggregation or outbreak time early warning program stored in the memory and operable on the processor, wherein when the processor executes the hospital infectious disease aggregation or outbreak time early warning method program, the hospital infectious disease aggregation or outbreak time early warning method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to execute a method of pre-warning of the time to aggregation or outbreak of a hospital infectious disease according to any one of claims 1 to 7.
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