CN111739652A - Epidemic situation prevention and control auxiliary decision-making method and system based on user time coding - Google Patents
Epidemic situation prevention and control auxiliary decision-making method and system based on user time coding Download PDFInfo
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Abstract
The invention provides an epidemic situation prevention and control auxiliary decision-making method and system based on user time coding, wherein the method comprises the following steps: collecting original data; summarizing key information in user original data by using a machine learning data analysis system; the summarized key information is presented on a user time code; the invention carries out epidemic situation decision through user time coding, extracts key words and carries out coding conversion through algorithms such as semantic recognition, intelligent image detection and the like by acquiring clinical data of a user in a hospital, and generates a user time coding library. And integrating the key information into a user time code according to the time sequence. The time codes of all users in the region are stored in a centralized mode, the users are collected according to a certain disease, the characteristics of the users and the disease conditions are extracted from the collected data, and/or the data are collected according to a certain keyword and a certain correlation, analysis results and epidemic situation prevention and control decision suggestions are given, and decision makers are assisted to make epidemic situation prevention and control decisions.
Description
Technical Field
The invention relates to a medical data analysis application software system, in particular to an epidemic prevention and control auxiliary decision-making method and system based on user time coding.
Background
When a patient visits a private clinic for a visit due to conditions of clinical symptoms common in many illnesses, such as fever and pain, the physician is generally unaware of whether the user meant the start of a particular infection outbreak that could lead to an epidemic.
To confirm an outbreak of an infectious disease, it is necessary to diagnose the condition of the patient. Subsequently, it is also necessary to observe whether the number of users diagnosed with the same disease or infectious disease suddenly increases.
Typically, this means that there should be many patients who have entered the hospital and whose condition has been diagnosed and confirmed. In addition, laboratory tests must be performed to identify infectious agents and determine the ease of transmission of the infectious disease. Furthermore, statistics need to be compiled because sometimes outbreaks can only be identified if a sufficient number of people in a population are infected.
Therefore, only after an outbreak, the doctor is notified of the occurrence of an infectious disease. Without a confirmed diagnosis, it is difficult for medical institutions to become predisposed to infectious diseases. There is a risk that some users may have been misdiagnosed with their infectious disease and have returned home without warning of the infectivity of their condition.
The existing epidemic situation prevention and control mechanism is that information is reported layer by layer, and a final decision maker can only see a small amount of information on a paper surface and cannot see first-line clinical data, so that the situation of an epidemic situation cannot be completely mastered, and the time machine for prevention and control is possibly missed. At present, a complete system does not exist, and an auxiliary decision suggestion can be given according to front-line diagnosis and treatment information of all users in an area, so that the probability of decision errors is high.
Disclosure of Invention
The invention mainly aims to provide an epidemic situation prevention and control auxiliary decision-making method and system based on user time coding, and aims to solve the problems that the existing epidemic situation prevention and control mechanism is that information is reported layer by layer, and a final decision-making person can only see a small amount of information on a paper surface and can not see first-line clinical data, so that the situation of an epidemic situation can not be completely mastered, and the prevention and control time machine can be missed.
The invention adopts the modes of extracting, converting and loading the original data of the user to establish a special user time code for each user, and any change on the user time code, including the examination result, the test report, the medication record and the intelligent detection result of the original image data, can be integrated to prompt the change of the health state of the user. And integrating the time codes of all users in the region, integrating, counting, classifying and analyzing the data, displaying the analysis result to a decision maker in a cockpit mode, and simultaneously giving a certain decision suggestion to assist the decision maker in making an epidemic prevention and control decision.
In order to achieve the purpose, the invention adopts the following technical scheme:
an epidemic situation prevention and control assistant decision-making method based on user time coding comprises the following steps:
step 1, analyzing the user original data by using a machine learning data analysis system, summarizing key information in the user original data, converting and recording the key information in a user time coding library; before step 1, acquiring user raw data, wherein the data comprises: electronic medical record data, image inspection data and inspection data; and synchronizing the user original data to the cloud system, and acquiring the user original data by utilizing a front server technology.
Further, the machine learning data analysis system includes: semantic recognition algorithm, ICD10 disease diagnosis code library, SNOMED CT clinical medical term code library, image intelligent detection, LOINC index identifier logic naming and code library, and vector code;
after the semantic recognition algorithm is adopted by the character information in the electronic medical record data, key information of the character information in the electronic medical record data is presented on a user time code through vector coding;
after the electronic medical record data passes through the ICD10 disease diagnosis code library, key information in the electronic medical record data is directly presented on a user time code;
the electronic medical record data directly presents key information in the electronic medical record data on a user time code through the SNOMED CT clinical medical term code library;
the image inspection data adopts image intelligent detection and then presents key information of the image inspection data on a user time code through vector coding;
and after passing through the LOINC index identifier logic naming and coding library, the checking data directly presents key information of the checking data on a user time code.
further, the user time code is displayed in a form of a chart;
preferably, the user time code constitutes the user time code library.
And 3, according to the user time code, integrating all the primary data contained in the primary data to classify, count and analyze to obtain the distribution state, the flow relation and the disease condition information of a certain infectious disease, displaying in a chart mode, and giving a grading reference and an auxiliary prevention and control decision suggestion.
Preferably, in step 3, all the user time codes in the region are stored in a centralized manner, the user time codes are collected according to a certain class of diseases, and the characteristics of the users and the disease conditions are extracted from the collected user time codes to perform classification, statistics and analysis, so that the distribution state, the flow relationship and the disease condition severity information of a certain infectious disease are obtained, displayed in a graph manner, and a grading reference and an auxiliary prevention and control decision suggestion are given.
Preferably, in step 3, according to the user time code and the supervision requirement, the collection time code is performed according to the keywords and/or some kind of association, the analysis result and the epidemic situation prevention and control decision suggestion are given, and the decision maker is assisted to make the epidemic situation prevention and control decision.
An epidemic situation prevention and control assistant decision-making system based on user time coding comprises: the system comprises an original data acquisition module, a machine learning data analysis system module, a user time coding library module and an epidemic situation prevention and control auxiliary decision module;
the original data acquisition module is in butt joint with electronic medical records, image examination data and inspection data of all hospitals in the jurisdiction, wherein the electronic medical record data is acquired by a hospital information system; the image inspection data is collected from a radiation information management system; the inspection data is collected from a laboratory information management system, and the original data is synchronized to the cloud;
the machine learning data analysis system module analyzes the collected original data, uses intelligent semantic recognition on the character information, uses intelligent detection on the image original data, and directly converts the inspection and medication records. After the machine learning data analysis system passes through, the machine learning data analysis system module sums up the key information of all users, and the key information is recorded in the user time coding library module after being converted;
and the user time coding library module updates the user time codes according to a time sequence by the key information summarized by the machine learning data analysis system module, each user has one user time code, the user time codes are displayed in a chart mode, the user time coding library consists of the user time codes, the diagnosis and treatment information of each time point on the user time codes is recorded in a pure digital code in the computer and is displayed outside the computer in a chart mode for people to read.
The epidemic prevention and control auxiliary decision-making module stores all user time codes in the region in a centralized manner, collects the codes according to a certain disease, extracts the characteristics of the users and the disease conditions from the codes or collects data according to a certain keyword and/or a certain association, gives an analysis result and an epidemic prevention and control decision suggestion, and assists a decision-making person to make an epidemic prevention and control decision.
Further, the certain keyword may be fever, dyspnea, or the like.
Further the certain association may be e.g. a place of residence, a user relationship, etc.
The invention establishes exclusive user time code for each user in the modes of extracting, converting and loading the original data of the user, and prompts the change of the health state of the user according to the user time code. And integrating time codes of all users in the region, integrating, counting, classifying and analyzing the data, displaying an analysis result to a decision maker in a cockpit mode, and simultaneously giving a certain decision suggestion to assist the decision maker in making an epidemic prevention and control decision.
Compared with the prior art, the epidemic situation prevention and control assistant decision-making method and system based on the user time code have the following beneficial effects:
1. the conventional epidemic prevention and control decision-making basis information in the prior art is reported layer by a hospital receiving a consultation, and the epidemic prevention and control auxiliary decision-making method based on the time coding of the user directly faces the first-line data to the decision-making person, so that the decision-making person can judge more intuitively;
2. the conventional epidemic situation prevention and control decision in the prior art is low in objectivity and has certain subjectivity, and the auxiliary decision for epidemic situation prevention and control of user time coding is high in objectivity and avoids interference of other factors;
3. the conventional epidemic prevention and control decision in the prior art has no data customization, the epidemic prevention and control auxiliary decision of the user time coding can carry out statistical analysis on data according to a certain keyword or a certain association, and information such as circulation relation, light and heavy grading and the like of diseases is given, so that the decision maker is more visual and more convenient to judge;
4. the conventional epidemic prevention and control decision in the prior art is influenced by various objective factors, so that the decision accuracy is not high, the influence of the objective factors in the epidemic prevention and control assistant decision of the user time coding is small, and the decision accuracy is high.
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FIG. 1 is a schematic diagram illustrating steps of an epidemic prevention and control aid decision-making method and system based on user time coding according to the present invention;
FIG. 2 is a block diagram of an epidemic prevention and control aid decision-making method and system based on user time coding according to the present invention;
FIG. 3 is a schematic flow chart of an epidemic prevention and control aid decision-making method and system based on user time coding according to the present invention;
reference numerals: the system comprises an electronic medical record data acquisition 1, an image inspection data acquisition 2, an inspection and medication record data acquisition 3, a semantic recognition algorithm 4, an ICD10 disease diagnosis coding library 5, a SNOMED CT clinical medical term coding library 6, an image intelligent detection 7, a LOINC index identifier logic naming and coding library 8, a vector code 9, a user time code 10, an epidemic prevention and control auxiliary decision 11 and a decision maker 12.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments. In the following description, characteristic details such as specific configurations and components are provided only to help the embodiments of the present invention be fully understood. Thus, it will be apparent to those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In various embodiments of the present invention, it should be understood that the sequence numbers of the following processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may be determined from a and/or other information.
Firstly, referring to fig. 1, a schematic step diagram of an epidemic prevention and control aid decision-making method and system based on user time coding, and referring to fig. 3, a schematic flow diagram of an epidemic prevention and control aid decision-making method and system based on user time coding according to the present invention;
the invention comprises the following steps:
step 1, using a machine learning data analysis system to sum up key information in user original data, wherein the original data is collected before step 1; the method specifically comprises the steps of electronic medical record data acquisition 1, image examination data acquisition 2 and inspection data acquisition 3, wherein the electronic medical record data is acquired from a hospital information system; the image inspection data is collected from a radiation information management system; the test data is collected from a laboratory information management system, the data collection is a general preposed server technology, the data can be synchronized to the cloud, or only the data is read and recorded key information, information which is acquired through user clinical data collection and meets the standard, such as disease diagnosis, test indexes, clinical terms and the like, is directly converted into codes, and the core of the data collection is nonstandard language conversion and intelligent detection result conversion of images.
The step 1 specifically comprises the following steps: the system comprises a semantic recognition algorithm 4, an ICD10 disease diagnosis coding library 5, an SNOMED CT clinical medical term coding library 6, an image intelligent detection 7, a LOINC index identifier logic naming and coding library 8 and a vector code 9, wherein after the semantic recognition algorithm 4 is adopted by the text information in the electronic medical record data, key information of the text information in the electronic medical record data is presented on a user time code 10 through the vector code 9; after a part of the electronic medical record data passes through the ICD10 disease diagnosis code library 5, key information in the electronic medical record data is directly presented on the user time code 10; one part of the electronic medical record data directly presents key information in the electronic medical record data on a user time code 10 through the SNOMED CT clinical medical term code library 6; after the image inspection data are intelligently detected 7 by adopting images, key information of the image inspection data is presented on a user time code 10 through a vector code 9; the key information of the check data is directly presented on the user time code 10 after the check data passes through the LOINC index identifier logic naming and coding library 8.
Further, the invention adopts a bidirectional semantic recognition coding algorithm in the semantic recognition algorithm to extract information about diseases in Chinese information in the electronic medical record, further extracts keywords about the diseases, carries out semantic recognition on the keywords, finds out near-meaning words in a standard library, carries out vector coding conversion on the near-meaning words, stores the near-meaning words in a computer in a digital coding mode, and displays the near-meaning words outside the computer in a graph mode for reading people.
Further, the image intelligent detection of the invention adopts a convolution neural network algorithm to extract the focus information in the image inspection data. The convolutional neural network algorithm can accurately identify focus information, the identified focus information is subjected to vector coding conversion and then stored in a computer in a digital coding mode, and the focus information is displayed outside the computer in a chart mode and read by people.
After the analysis, the machine learning data analysis summarizes the key information of all users, and records the key information in the user time code library after conversion.
and 2, updating user time codes according to a time sequence by the key information subjected to machine learning data analysis and summation, wherein each user has one user time code, and the diagnosis and treatment information of each time point on the user time codes is recorded in a computer in a pure digital code mode and displayed outside the computer in a chart mode for human eyes to read.
in step 3, all the user time codes in the region are stored in a centralized manner, the user time codes are collected according to a certain class of diseases, the characteristics of the users and the disease conditions are extracted from the collected user time codes, classification, statistics and analysis are carried out, the distribution state, the flow relationship and the disease condition information of a certain infectious disease are obtained, the information is displayed in a chart manner, and grading reference and auxiliary prevention and control decision suggestions are given.
In another mode, in step 3, according to the user time code and the supervision requirement, the grouping time code is carried out according to keywords and/or certain association, an analysis result is given, an epidemic situation prevention and control decision suggestion is given at the same time, and a decision maker is assisted to make an epidemic situation prevention and control decision, wherein a certain keyword can be fever, dyspnea and the like; some association may be, for example, a place of residence, a user relationship, etc.
Referring to fig. 2, a schematic structural diagram of an epidemic prevention and control aid decision-making method and system based on user time coding according to the present invention is shown; the invention comprises four modules: the invention adopts the modes of extracting, converting and loading the original data of the user, establishes a special user time code for each user, and integrates any change in the time code, including inspection results, test reports, medication records and intelligent detection results of the original image data to prompt the change of the health state of the user. And integrating the time codes of all users in the region, integrating, counting, classifying and analyzing the data, displaying the analysis result to a decision maker in a cockpit mode, and giving a decision suggestion to assist the decision maker in making an epidemic prevention and control decision.
Each module was analyzed as follows:
the original data acquisition module is used for acquiring electronic medical records, image examination and inspection butt joint of all hospitals in the jurisdiction, and synchronizing the acquired original data to the cloud;
the machine learning data analysis system module analyzes the collected original data, uses intelligent semantic recognition to the character information, uses intelligent detection to the image original data, and directly converts the inspection and medication records. After the machine learning data analysis system passes through, the machine learning data analysis system module sums up the key information of all users, and the key information is recorded in the user time coding library module after being converted; the invention makes the two-way semantic recognition coding algorithm carry out semantic recognition on the keywords, finds out the near meaning words in the standard library and carries out coding conversion on the near meaning words, images comprise X-ray images, CT images and the like and contain hidden disease information, the invention uses the convolutional neural network algorithm to extract focus information, can accurately recognize focuses and informs information of the properties, the grades and the like of the focuses in a coding mode;
the user time code library module is composed of the user time codes, each user has one user time code, and the user time codes are displayed in a chart mode; the user time code records all changes in clinical information. The user time coding can be said to be the combination of semantic recognition, intelligent detection of images and standard coding. The time code of the user needs to be synchronous with the information in the hospital according to a certain period;
the epidemic situation prevention and control auxiliary decision-making module is used for storing all user time codes in an area in a centralized manner, collecting the time codes according to a certain type of diseases, extracting the characteristics of users and disease conditions from the time codes or collecting data according to a certain keyword and/or certain correlation, wherein the keyword can be fever, dyspnea and cough, classifying and counting the users according to the given keyword, carrying out comparative analysis and analyzing the proportion of the expression symptoms of the diseases; the relevant words can be the relationship between the residence and the users, the users are classified and counted according to the given relevant words, comparative analysis is carried out, the distribution conditions and the close relationship among the people of the disease areas are analyzed, and epidemic situation prevention and control decision suggestions are carried out according to the analysis results, for example: the cough symptom accounts for 100%, and the given prevention and control decision suggestion needs to wear a mask, so that the air transmission is reduced, and the decision maker is effectively assisted to make epidemic prevention and control decisions.
The invention generates the user time coding library by collecting the clinical data of the user in the hospital, including information such as electronic medical record, image examination, chemical examination, medication and the like, extracting key words and carrying out coding conversion through algorithms such as semantic recognition, intelligent image detection and the like. And integrating the key information into a user time code according to the time sequence. The time codes of all users in the region are stored in a centralized mode, the users are collected according to a certain disease, the characteristics of the users and the disease conditions are extracted from the collected data, and/or the data are collected according to a certain keyword and a certain correlation, analysis results and epidemic situation prevention and control decision suggestions are given, and decision makers are assisted to make epidemic situation prevention and control decisions.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the invention has been described in detail with reference to the foregoing illustrative embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. An epidemic situation prevention and control assistant decision-making method based on time coding is characterized by comprising the following steps:
step 1; analyzing the user original data through a machine learning data analysis system, summarizing key information in the user original data, and recording the summarized key information in a user time coding library after conversion;
step 2; determining key information in the user original data on a user time code according to a time sequence;
step 3; and according to the user time code, integrating all the primary data of the lines contained in the user time code to classify, count and analyze, obtaining key information, and giving a grading reference and an auxiliary prevention and control decision suggestion.
2. The epidemic prevention and control aid decision-making method based on time coding according to claim 1, characterized in that, before step 1, user raw data is collected, the raw data includes: electronic medical record data, image inspection data and inspection data; the user raw data is synchronized to the cloud system.
3. The epidemic prevention and control aided decision making method based on time coding as claimed in claim 2, wherein in step 1, the user raw data collection is collected by using a front-end server technology.
4. The epidemic prevention and control aided decision making method based on time coding according to claim 1, wherein the machine learning data analysis system comprises: semantic recognition algorithm, ICD10 disease diagnosis code library, SNOMED CT clinical medical term code library, image intelligent detection, LOINC index identifier logic naming and code library, and vector code;
after the character information in the electronic medical record data is identified by the semantic identification algorithm, key information of the character information in the electronic medical record data is presented on a user time code through the vector code;
after the electronic medical record data passes through the ICD10 disease diagnosis code library, key information in the electronic medical record data is directly presented on a user time code;
the electronic medical record data directly presents key information in the electronic medical record data on a user time code through the SNOMED CT clinical medical term code library;
the image inspection data adopts image intelligent detection and then presents key information of the image inspection data on a user time code through vector coding;
and after passing through the LOINC index identifier logic naming and coding library, the checking data directly presents key information of the checking data on a user time code.
5. The epidemic prevention and control aid decision-making method based on time coding according to claim 1, characterized in that in step 3, all the user time codes in the area are stored in a centralized manner, and are collected according to categories, and the characteristics of the users are extracted from the collected user time codes for classification, statistics, analysis, and obtaining distribution state, flow relationship, and weight information, which are displayed in a graph manner, and a hierarchical reference and aid prevention and control decision-making suggestion are given.
6. The epidemic prevention and control aid decision-making method based on time coding according to claim 1, characterized in that in step 4, according to the user time coding and supervision requirements, the collection time coding is performed according to keywords and/or some kind of association, analysis results and epidemic prevention and control decision suggestions are given, and decision-makers are assisted to make epidemic prevention and control decisions.
7. An epidemic situation prevention and control assistant decision-making system based on user time coding is characterized by comprising: the system comprises an original data acquisition module, a machine learning data analysis system module, a user time coding library module and an epidemic situation prevention and control auxiliary decision module; the original data acquisition module acquires original data and transmits the original data to the machine learning data analysis system module, and key information in the original data is displayed on a user time code after the original data is analyzed by the machine learning data analysis system module; the epidemic situation prevention and control auxiliary decision-making module utilizes the user time coding library module to make an epidemic situation prevention and control decision;
the original data acquisition module is in butt joint with electronic medical records, image examination data and inspection data of all hospitals in the jurisdiction, wherein the electronic medical record data is acquired by a hospital information system; the image inspection data is collected from a radiation information management system; the inspection data is collected from a laboratory information management system, and the original data is synchronized to the cloud;
the machine learning data analysis system module analyzes the collected original data, uses intelligent semantic recognition on the text information, uses intelligent detection on the image original data, and directly converts the inspection and medication records, and after passing through the machine learning data analysis system, the machine learning data analysis system module summarizes the key information of all users and records the summarized key information in the user time coding library module after conversion;
the user time coding library module updates the user time codes according to a time sequence by the key information summarized by the machine learning data analysis system module, each user has one user time code, the user time codes are displayed in a chart mode, and the user time coding library consists of the user time codes; the user time code is used for recording the diagnosis and treatment information of each time point in a computer in a digital code mode by taking time as a sequence, and displaying the diagnosis and treatment information to human eyes for reading by a diagram mode outside the computer;
the epidemic prevention and control auxiliary decision-making module stores all user time codes in the region in a centralized manner, collects the codes according to a certain disease, extracts the characteristics of the users and the disease conditions or collects data according to a certain keyword and/or a certain association, gives an analysis result and an epidemic prevention and control decision suggestion, and assists decision-makers in making epidemic prevention and control decisions.
8. The system according to claim 7, wherein the keyword is fever, dyspnea, or the like.
9. The system of claim 7, wherein the relationship is a residential area, a user relationship, or the like.
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