WO2023029506A1 - Procédé et appareil d'analyse d'état de maladie, dispositif électronique et support de stockage - Google Patents

Procédé et appareil d'analyse d'état de maladie, dispositif électronique et support de stockage Download PDF

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WO2023029506A1
WO2023029506A1 PCT/CN2022/087710 CN2022087710W WO2023029506A1 WO 2023029506 A1 WO2023029506 A1 WO 2023029506A1 CN 2022087710 W CN2022087710 W CN 2022087710W WO 2023029506 A1 WO2023029506 A1 WO 2023029506A1
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data
information
treatment
target
disease
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Chinese (zh)
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成芳
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康键信息技术（深圳）有限公司
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    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G06F40/295—Named entity recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • 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

Definitions

  • the present application relates to the field of artificial intelligence and digital medical technology, and in particular to a disease analysis method, device, electronic equipment and storage medium.
  • the main purpose of the embodiments of the present application is to provide a disease analysis method, device, electronic equipment and storage medium, aiming to obtain reference pathological data by analyzing the patient's historical disease, and improve the efficiency of disease analysis.
  • the embodiment of the present application proposes a disease analysis method, the method comprising:
  • a condition analysis report is generated according to the diagnostic conclusion label.
  • the embodiment of the present application proposes a disease analysis device, which includes:
  • Electronic medical record data acquisition module used to obtain electronic medical record data
  • a feature extraction module configured to extract entity features from the electronic medical record data to obtain target disease information
  • a processing module configured to use a pre-trained disease recognition model to process the target disease information, and generate a treatment reminder corresponding to the target disease information;
  • the treatment feedback data receiving module is used to receive the treatment feedback data that the client responds to according to the treatment reminder;
  • a diagnosis conclusion label generating module configured to generate a diagnosis conclusion label according to the treatment feedback data and the target condition information
  • a condition analysis report generating module configured to generate a condition analysis report according to the diagnosis conclusion label.
  • the embodiment of the present application provides an electronic device, the electronic device includes a memory, a processor, a program stored in the memory and operable on the processor, and a program for implementing the processor
  • a data bus connecting and communicating with the memory when the program is executed by the processor, a disease analysis method is implemented, wherein the disease analysis method includes: acquiring electronic medical record data; Perform entity feature extraction to obtain target condition information; use a pre-trained condition recognition model to process the target condition information to generate a treatment reminder corresponding to the target condition information; receive treatment feedback from the user terminal based on the treatment reminder response data; generate a diagnosis conclusion label according to the treatment feedback data and the target condition information; generate a condition analysis report according to the diagnosis conclusion label.
  • the embodiment of the present application provides a computer-readable storage medium for computer-readable storage, the computer-readable storage medium stores one or more programs, and the one or more programs can be stored by one Or executed by multiple processors to implement a disease analysis method, wherein the disease analysis method includes: obtaining electronic medical record data; performing entity feature extraction on the electronic medical record data to obtain target disease information; using pre-trained disease
  • the recognition model processes the target condition information to generate a treatment reminder corresponding to the target condition information; receives treatment feedback data from the user end according to the treatment reminder response; according to the treatment feedback data and the target condition information, Generate a diagnosis conclusion label; generate a condition analysis report according to the diagnosis conclusion label.
  • the disease analysis method, device, electronic equipment and storage medium proposed in this application obtain electronic medical record data and perform entity feature extraction on electronic medical record data to obtain target disease information.
  • This method can realize feature extraction of electronic medical record data. Reducing the total amount of data makes it easier to extract the required disease information; then use the pre-trained disease identification model to process the target disease information, and generate treatment reminders corresponding to the target disease information, so that users can take medication according to the treatment reminder or Seek medical attention.
  • Fig. 1 is the flowchart of the condition analysis method provided by the embodiment of the present application.
  • Fig. 2 is the flowchart of step S102 in Fig. 1;
  • Fig. 3 is the flowchart of step S103 in Fig. 1;
  • Fig. 4 is a partial flowchart of a disease analysis method provided by another embodiment of the present application.
  • Fig. 5 is the flowchart of step S105 in Fig. 1;
  • Fig. 6 is the flowchart of step S106 in Fig. 1;
  • Fig. 7 is a schematic structural diagram of a disease analysis device provided by an embodiment of the present application.
  • FIG. 8 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present application.
  • Artificial Intelligence It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science. Intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a manner similar to human intelligence. Research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
  • Natural language processing uses computers to process, understand and use human languages (such as Chinese, English, etc.). NLP belongs to a branch of artificial intelligence and is an interdisciplinary subject between computer science and linguistics. Known as computational linguistics. Natural language processing includes syntax analysis, semantic analysis, text understanding, etc. Natural language processing is often used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information retrieval, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining. It involves language processing Related data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research and linguistics research related to language computing, etc.
  • Information Extraction A text processing technology that extracts specified types of factual information such as entities, relationships, and events from natural language texts, and forms structured data output.
  • Information extraction is a technique to extract specific information from text data.
  • Text data is composed of some specific units, such as sentences, paragraphs, and chapters.
  • Text information is composed of some small specific units, such as words, words, phrases, sentences, paragraphs, or combinations of these specific units. . Extracting noun phrases, personal names, and place names in text data is all text information extraction.
  • the information extracted by text information extraction technology can be various types of information.
  • Association analysis is a simple and practical analysis technique, which is to discover the association or correlation existing in a large number of data sets, so as to describe the laws and patterns of the simultaneous appearance of certain attributes in a thing. Association analysis is to discover interesting associations and correlations between item sets from a large amount of data.
  • Support count An item set appears in several transactions, and its support count is how many.
  • Strong association rules Rules greater than or equal to the minimum support threshold and minimum confidence threshold are called strong association rules. The ultimate goal of association analysis is to find out strong association rules.
  • Apriori algorithm It is a basic algorithm for mining frequent itemsets required to generate Boolean association rules.
  • Apriori property Any subset of a frequent itemset should also be a frequent itemset. Prove that by definition, if an itemset I does not meet the minimum support threshold min_sup, then I is not frequent, that is, P(I) ⁇ min_sup. If an item A is added to the item set I, the resulting new item set (I ⁇ A) is not frequent, and the number of occurrences in the entire transaction database cannot be more than the number of occurrences of the original item set I, so P (I ⁇ A) ⁇ min_sup, that is (I ⁇ A) is not frequent. In this way, it can be easily confirmed that the Apriori property holds according to the inverse axiom.
  • FP-tree After Pattern Tree, referred to as FP-tree; It is an algorithm for discovering frequent patterns based on frequent pattern trees.
  • FP-growth algorithm by scanning the transaction database twice, each transaction The included frequent items are compressed and stored in FP-tree in descending order of their support.
  • the process of discovering frequent patterns in the future there is no need to scan the transaction database, but only to search in the FP-Tree, and the frequent pattern is directly generated by recursively calling the FP-growth method, so the whole discovery process is also Candidate patterns need not be generated.
  • Collaborative filtering algorithm It is a relatively well-known and commonly used recommendation algorithm. It discovers the user's preferences based on the mining of user historical behavior data, and predicts the products that users may like to recommend, or finds similar users (based on users) or Items (based on items). The realization of the user-based collaborative filtering algorithm mainly needs to solve two problems. One is how to find people who have similar hobbies as you, that is, to calculate the similarity of data.
  • BERT Bit Encoder Representations from Transformers: It is a language representation model (language representation model). BERT uses the Transformer Encoder block for connection, which is a typical two-way encoding model.
  • MLP Multilayer Perceptron
  • An MLP can be viewed as a directed graph consisting of multiple layers of nodes, each fully connected to the next layer. Except for the input node, each node is a neuron (or processing unit) with a nonlinear activation function.
  • MLP is an extension of the perceptron, which overcomes the weakness that the perceptron cannot recognize linearly inseparable data.
  • the simplest MLP is a three-layer structure (input layer-hidden layer-output layer). The layers of the multi-layer perceptron are fully connected, that is, any neuron in each layer is connected to all neurons in the previous layer. This connection actually represents a weight summation.
  • BP Error backpropagation algorithm
  • BP Error backpropagation algorithm
  • This method computes the gradient of the loss function for all weights in the network. This gradient is fed back to the optimization method to update the weights to minimize the loss function.
  • BP algorithm is suitable for a learning algorithm of multi-layer neural network, which is based on the gradient descent method.
  • the input-output relationship of the BP network is essentially a mapping relationship: the function completed by a BP neural network with n inputs and m outputs is a continuous mapping from n-dimensional Euclidean space to a finite field in m-dimensional Euclidean space.
  • the learning process of BP algorithm is composed of forward propagation process and back propagation process.
  • the input information is processed layer by layer through the hidden layer through the input layer and transmitted to the output layer. If the desired output value cannot be obtained at the output layer, take the sum of the squares of the output and the expected error as the objective function, transfer to backpropagation, and calculate the partial derivative of the objective function with respect to the weight of each neuron layer by layer to form the objective
  • the gradient of the function to the weight vector is used as the basis for modifying the weight, and the learning of the network is completed in the process of modifying the weight. When the error reaches the expected value, the network learning ends.
  • Logistic function (Logistic function or Logistic curve): Logistic function or Logistic curve is a common S-shaped function, and the generalized Logistic curve can imitate the S-shaped curve of population growth (P) in some cases.
  • P population growth
  • AI artificial intelligence
  • digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
  • Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics.
  • Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning.
  • the embodiment of the present application can realize the analysis of the patient's historical condition based on the medical cloud technology.
  • medical cloud refers to the use of "cloud computing" to create a medical and health service cloud based on new technologies such as cloud computing, mobile technology, multimedia, 4G communication, big data, and the Internet of Things, combined with medical technology.
  • the platform realizes the sharing of medical resources and the expansion of medical coverage.
  • medical cloud improves the efficiency of medical institutions and facilitates residents to seek medical treatment. For example, appointment registration, electronic medical records, and medical insurance in hospitals are all products of the combination of cloud computing and the medical field.
  • Medical cloud also has the advantages of data security, information sharing, dynamic expansion, and overall layout.
  • the embodiment of the present application provides a condition analysis method, device, electronic equipment and storage medium, which can obtain a condition analysis report by analyzing the patient's historical condition, and provide reference pathological data for the subsequent diagnosis process, improving the The efficiency of disease analysis can also reduce the cost of medical treatment and medication for patients.
  • the disease analysis method, device, electronic device, and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the disease analysis method in the embodiments of the present application is described.
  • the disease analysis method provided in the embodiment of the present application relates to the fields of artificial intelligence and digital medical technology.
  • the disease analysis method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running on a terminal or a server.
  • the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.
  • the server end can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or It can be configured as a cloud that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.
  • the server; the software can be the application of the disease analysis method, etc., but is not limited to the above forms.
  • Fig. 1 is an optional flowchart of the disease analysis method provided by the embodiment of the present application.
  • the method in Fig. 1 may include but not limited to include steps S101 to S106.
  • Step S101 obtaining electronic medical record data
  • Step S102 extracting entity features from electronic medical record data to obtain target disease information
  • Step S103 using the pre-trained disease identification model to process the target disease information, and generate a treatment reminder corresponding to the target disease information;
  • Step S104 receiving the treatment feedback data that the client responds to according to the treatment reminder
  • Step S105 generating a diagnosis conclusion label according to the treatment feedback data and the target condition information
  • Step S106 generating a disease analysis report according to the diagnosis conclusion label.
  • an electronic medical record is generated according to the personal historical medical records uploaded by the patient, and the electronic medical record data is obtained, wherein the electronic medical record data includes patient information, medical records, medication records and so on.
  • the entity feature extraction is performed on the electronic medical record data to obtain the target condition information, among which the target condition information includes the patient's age, gender, basic health indicators, medical diagnosis data, medication records, adverse drug reaction information, operation records, etc.
  • This method can realize the feature extraction of electronic medical record data, reduce the total amount of data, and make it easier to extract the required disease information.
  • the pre-trained condition recognition model to process the above target condition information, identify the patient's historical target condition, generate a treatment reminder corresponding to the target condition information, and feed back this treatment reminder to the patient, so that the patient can follow the treatment reminder Seek medical attention or take medication. After the treatment reminder is fed back to the patient, it is also necessary to obtain treatment feedback from the patient, for example, the treatment feedback data that the user terminal responds to according to the treatment reminder can be received.
  • the diagnostic conclusion label By analyzing the treatment feedback data and target condition information, the diagnostic conclusion label can be obtained, and the treatment effect can be evaluated more conveniently; it should be noted that when analyzing the treatment feedback data and target condition information, a big data analysis model can be used To match the treatment feedback data with the target condition information, generate the corresponding diagnostic conclusion label according to the matching situation, and finally generate the condition analysis report according to the diagnosis conclusion label, and obtain the condition analysis report by analyzing the patient's historical condition, which will be used for the subsequent diagnosis process Provide reference pathological data, improve the efficiency of disease analysis, and also reduce the cost of medical treatment and medication for patients.
  • the above data is medical data, such as personal health records, prescriptions, examination reports and other data.
  • step S102 may include but not limited to include steps S201 to S205:
  • Step S201 extracting natural language text in electronic medical record data
  • Step S202 using a preset lexical analysis model to identify entity features in the natural language text
  • Step S203 segmenting the natural language text to obtain a natural language vocabulary sequence
  • Step S204 constructing a list of feature sequences according to entity features and natural language vocabulary sequences
  • Step S205 determine the target condition information according to the feature sequence table.
  • the unstructured data in the electronic medical record data is first converted into unified structured data, and the required natural language text is extracted from the structured data.
  • the preset lexical analysis model uses the preset lexical analysis model to identify entity features in natural language text.
  • a medical thesaurus is pre-built, and the medical thesaurus may include medical names, medical terms, non-medical names, folk names, international medical terms, etc. related to various medical pathologies.
  • the preset lexical analysis model can enumerate medical specific names.
  • Input the natural language text into the preset lexical analysis model, and identify the entity features in the natural language text through the medical specific names contained in the preset lexical analysis model and the preset part-of-speech categories, and the entity features may include
  • the above-mentioned multi-dimensional entity vocabulary related to medical pathology such as medical names, medical terms, non-medical names, folk names, international medical terms, modifiers, and time information.
  • a sequence classifier can also be built based on the bi-LSTM algorithm. In the model based on the bi-LSTM algorithm, the word wi and character embedding are input, and the long and short memory from left to right and the length from right to left are used.
  • the sequence classifier can pass the input entity features directly to the softmax classifier through this output layer, and create a probability distribution on the preset label through the softmax classifier, so as to mark and classify the entity parameters according to the probability distribution, and finally
  • the entity features after the classification process are subjected to feature extraction to obtain the required entity features.
  • the BERT encoder can also be used to convert the entity feature string from text form to encoded form through the preset encoding function to realize the storage of entity features.
  • a corresponding part-of-speech is assigned to a word segment in the natural language text, such as noun, verb, adjective, and so on.
  • the continuous natural language text is segmented into natural language lexical sequences with semantic rationality and integrity.
  • a feature sequence table is constructed, and the data in the electronic medical record data can be searched and compared according to the feature sequence table, and the target disease information can be determined more conveniently.
  • the above-mentioned natural language text is a medical text
  • the medical text can be an electronic healthcare record (Electronic Healthcare Record), an electronic personal health record, including medical records, electrocardiograms, medical images, etc.
  • An electronic record of value is a medical text
  • the medical text can be an electronic healthcare record (Electronic Healthcare Record), an electronic personal health record, including medical records, electrocardiograms, medical images, etc.
  • An electronic record of value is a medical text
  • the medical text can be an electronic healthcare record (Electronic Healthcare Record), an electronic personal health record, including medical records, electrocardiograms, medical images, etc.
  • An electronic record of value is an electronic healthcare record of value.
  • step S103 may include but not limited to include step S301 or step S303:
  • Step S301 inputting the target condition information into the pre-trained condition identification model
  • Step S302 performing quadrant partitioning on the target disease information through the disease identification model to obtain the partitioned disease information
  • Step S303 performing a fitting process on the partitioned disease information of each quadrant to generate a medication reminder or a medical reminder.
  • the BP algorithm can be used for deep learning to establish a disease recognition model, and the target disease information is input into the pre-trained disease recognition model.
  • the information is divided into tasks, and the condition information of the partitions is obtained.
  • the disease data in the target disease information is prioritized to obtain partitioned disease information of different importance levels.
  • the condition information of each quadrant is fitted through the fitting function, and the fitting result is output to the appearance layer for the judgment of accuracy and excellent agent for iterative optimization.
  • the iterative optimization meets the preset
  • the fitting result is output, and the corresponding medication reminder or medical reminder is generated according to the fitting result.
  • the preset iteration condition may be that the number of iterations reaches the preset number, etc., or other conditions, and is not limited thereto.
  • the disease recognition model is a multi-layer neural network, which contains two layers of processing units and two hidden layers, and each feedback can only be sent to the previous output layer or hidden layer.
  • the multi-layer neural network is a neural network that uses a backward propagation algorithm to learn classification or prediction. Specifically, the construction process of the disease recognition model is as follows:
  • w ij is the weight of the connection from unit i to unit j in the previous layer
  • O i is the output of unit i in the previous layer
  • ⁇ j is the bias of unit j, which is used as a threshold to change the unit’s active
  • Step 5 Calculate the errors of hidden nodes and output nodes respectively, where the output layer error formula is shown in formula (3); the hidden layer formula is shown in formula (4);
  • O j is the actual output of unit j
  • T j is the known target value of j given the training tuple.
  • O j (1-O j ) is the derivative of the logistic function
  • wjk is the weight of the connection from unit k to unit j in the next higher layer, and is the error of unit k.
  • Step 6 Extract the eliminated agents and recycle the corresponding root parts of speech after segmenting the corresponding natural language text.
  • a condition identification model that meets the requirements can be constructed, and then the reference condition information is input into the condition identification model, and the reference condition information is divided into quadrants through the condition identification model, and the reference condition information is assigned tasks according to the four-quadrant rule. Differentiate, and classify the disease data in the reference disease information according to priority, and obtain the partitioned disease information of different importance levels. Furthermore, the fitting function is used to fit the partition reference disease information of each quadrant, and the fitting result is output to the appearance layer for the judgment of accuracy and excellent agent for iterative optimization. When the iterative optimization meets the expected When the iterative condition is set, the iterative optimization is stopped, so as to complete the training of the pathological recognition model.
  • the patient's historical target condition is identified through the trained condition recognition model.
  • the target condition information is input into the trained condition recognition model to obtain the fitting result, and the target condition information is generated according to the fitting result.
  • the corresponding treatment reminder is given, and the treatment reminder is fed back to the patient, so that the patient can seek medical treatment or take medicine according to the treatment reminder.
  • the method may include, but is not limited to, step S401 to step S402:
  • Step S401 analyzing the drug data in the target condition information to obtain drug reaction information; wherein, the drug reaction information includes adverse drug reaction information;
  • Step S402 identifying abnormal medication data in the treatment feedback data according to the adverse drug reaction information.
  • the automatic text generation model of words is used to extract drug data, and the automatic text generation model can perform different data processing according to the type of input data.
  • Input keywords or text sentences or fields in historical medical records and historical medication records to the pre-trained text automatic generation model if the input keywords, text sentences or fields can match the preset reference text, it indicates The current input meets the requirements. If the current input is a keyword, select the same sentence set as the input keyword in the basic corpus, and generate the corresponding drug data field according to this sentence set.
  • the current input is a text sentence or field
  • the candidate sentence is copied and supplemented, and the drug data field is generated according to the supplemented candidate sentence.
  • these drug data fields are analyzed to obtain drug response information, which includes adverse drug reaction information, drug efficacy information, and medication guidance information. wait. Furthermore, by comparing and analyzing the adverse drug reaction information and the treatment feedback data, if the treatment feedback data involves data that matches the adverse drug reaction information, it can be determined that the data is abnormal drug use data, which can be more conveniently identified Abnormal medication data, and mark the abnormal medication data in the treatment feedback data, so as to play a prompt role in subsequent diagnosis and treatment, avoid the occurrence of abnormal medication again, and improve the reliability of medication.
  • step S104 the method also includes, but is not limited to:
  • the treatment feedback data and target condition information can be written into local files through the logback component (open source log component) in the data management layer, and the interface log including treatment feedback data and target condition information can be recorded through the interceptor.
  • the log collection system (Flume) in the management layer visualizes the treatment feedback data and the target disease information respectively to obtain the corresponding target visualization data, and then inputs these target visualization data into the Hive database and the HBase database in the data management layer for integration , and finally import the target visualization data from the PostgreSql database into the Hive database through the Sqoop transmission component in the data management layer, generate corresponding charts, and sort the series of charts according to the preset part-of-speech ranking sequence to generate visual data picture.
  • the preset part-of-speech level is that the level of nouns is higher than that of verbs, and the level of verbs is higher than that of adjectives.
  • step S105 in some embodiments may include, but is not limited to, step S501 to step S502:
  • Step S501 encoding treatment feedback data and target condition information respectively to obtain treatment feedback data in encoded form and target condition information in encoded form;
  • Step S502 using the preset big data analysis model to perform data analysis on the coded treatment feedback data and the coded target condition information to generate a diagnostic conclusion label.
  • the treatment feedback data and the target condition information can be respectively encoded by a preset encoder
  • the preset encoder can be a BERT-based encoder, that is, by obtaining the treatment feedback data and the target condition information , and tokenize treatment feedback data and target condition information, build a BERT token generator, pre-train the BERT token generator, and form a BERT encoder that meets the requirements, so that the BERT encoder can pass the preset
  • the encoding function converts the treatment feedback data and the target condition information from the text form into the code form, and obtains the treatment feedback data in the code form and the target condition information in the code form.
  • the collaborative filtering algorithm in the preset big data analysis model is used to calculate the similarity between the treatment feedback data in the coded form and the target disease information in the coded form. According to the degree of similarity, the treatment feedback data in the coded form and the target disease information in the coded form are correlated and matched to generate corresponding diagnostic conclusion labels.
  • the collaborative filtering algorithm may be a Jaccard similarity coefficient method, an included angle cosine method, or a similarity measurement method such as Euclidean distance or Manhattan distance, without limitation.
  • the preset big data analysis model may also use an association analysis algorithm to associate and match the encoded treatment feedback data and the encoded target disease information. Commonly used correlation analysis algorithms include Apriori algorithm, FP-growth algorithm and so on. This method improves the efficiency of data analysis, and also improves the matching accuracy of treatment feedback data and target disease information.
  • step S106 may include but not limited to include steps S601 to S603:
  • Step S601 purifying the diagnostic conclusion label according to the target condition information to obtain the purified diagnostic conclusion label
  • Step S602 verifying and analyzing the purified diagnosis conclusion label to obtain a standard diagnosis conclusion label
  • Step S603 generating a disease analysis report according to the standard diagnosis conclusion label.
  • the select statement function can also be preset, fill in the required option data in the preset select statement function, and use the select statement function filled with option data to extract the target disease information.
  • the data set is compared with the diagnosis conclusion label, and the abnormal diagnosis conclusion label is eliminated to obtain the purified diagnosis conclusion label.
  • the judgment of the abnormal diagnosis conclusion label can be determined according to the similarity between the data set and the diagnosis conclusion label. If the similarity between the two is less than the preset similarity threshold, the diagnosis conclusion label is determined to be an abnormal diagnosis conclusion label.
  • the purified diagnostic conclusion label is verified and analyzed, the target condition information and treatment feedback data corresponding to the purified diagnostic conclusion label are reviewed, and the target condition information and treatment feedback data are corrected and adjusted to obtain a standard diagnosis conclusion label.
  • the condition analysis report is generated according to the standard diagnosis conclusion label, and the condition analysis report is obtained by analyzing the patient's historical condition, which provides reference pathological data for the subsequent diagnosis process, improves the efficiency of condition analysis, and can also reduce the cost of medical treatment for patients and drug costs.
  • this method can realize feature extraction of electronic medical record data, reduce the total amount of data, and make it more convenient to extract the required data.
  • the patient s condition information; and then use the pre-trained condition recognition model to process the target condition information, and generate a treatment reminder corresponding to the target condition information, so that the patient can take medication or seek medical treatment according to the treatment reminder.
  • the embodiment of the present application also provides a disease analysis device, which can implement the above disease analysis method, the device includes:
  • the processing module 703 is configured to use the pre-trained disease recognition model to process the target disease information, and generate a treatment reminder corresponding to the target disease information;
  • the treatment feedback data receiving module 704 is used to receive the treatment feedback data that the client responds to according to the treatment reminder;
  • the diagnosis conclusion label generation module 705 is used for generating diagnosis conclusion labels according to the treatment feedback data and the target condition information
  • the disease analysis report generating module 706 is configured to generate a disease analysis report according to the diagnosis conclusion label.
  • the embodiment of the present application also provides an electronic device, the electronic device includes: a memory, a processor, a program stored in the memory and operable on the processor, and a data bus for realizing connection and communication between the processor and the memory , when the program is executed by the processor, the above disease analysis method is realized.
  • the electronic device may be any intelligent terminal including a tablet computer, a vehicle-mounted computer, and the like.
  • FIG. 8 illustrates a hardware structure of an electronic device in another embodiment.
  • the electronic device includes:
  • the processor 801 may be implemented by a general-purpose CPU (Central Processing Unit, central processing unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute related programs to realize The technical solutions provided by the embodiments of the present application;
  • a general-purpose CPU Central Processing Unit, central processing unit
  • a microprocessor an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute related programs to realize The technical solutions provided by the embodiments of the present application;
  • ASIC Application Specific Integrated Circuit
  • the memory 802 may be implemented in the form of a read-only memory (ReadOnlyMemory, ROM), a static storage device, a dynamic storage device, or a random access memory (RandomAccessMemory, RAM).
  • the memory 802 can store operating systems and other application programs.
  • the relevant program codes are stored in the memory 802, and are invoked by the processor 801 to execute a condition Analytical method;
  • the input/output interface 803 is used to realize information input and output
  • the communication interface 804 is used to realize the communication interaction between the device and other devices, and the communication can be realized through a wired method (such as USB, network cable, etc.), or can be realized through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.); and
  • a bus 805 which transmits information between various components of the device (such as a processor 801, a memory 802, an input/output interface 803, and a communication interface 804);
  • the processor 801 , the memory 802 , the input/output interface 803 and the communication interface 804 are connected to each other within the device through the bus 805 .
  • condition analysis method includes:
  • An embodiment of the present application also provides a computer-readable storage medium for computer-readable storage.
  • the computer-readable storage medium may be non-volatile or volatile.
  • the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement a disease analysis method, wherein the disease analysis method includes: obtaining electronic medical record data; Extract entity features from medical record data to obtain target condition information; use pre-trained condition recognition model to process target condition information to generate treatment reminders corresponding to target condition information; receive treatment feedback data from user terminals based on treatment reminder responses; Feedback data and target condition information to generate a diagnosis conclusion label; generate a condition analysis report based on the diagnosis conclusion label.
  • memory can be used to store non-transitory software programs and non-transitory computer-executable programs.
  • the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices.
  • the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.
  • the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including multiple instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method in each embodiment of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), magnetic disk or optical disc, etc., which can store programs. medium.
  • ROM read-only memory
  • RAM random access memory
  • magnetic disk or optical disc etc., which can store programs. medium.

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Abstract

La présente invention concerne un procédé et un appareil d'analyse d'état de maladie, un dispositif électronique, ainsi qu'un support de stockage. Le procédé consiste à : acquérir des données de dossier médical électronique (S101) ; effectuer une extraction de caractéristique d'entité sur les données de dossier médical électronique pour obtenir des informations d'état de maladie cibles (S102) ; traiter les informations d'état de maladie cibles en utilisant un modèle de reconnaissance d'état de maladie pré-entraîné pour générer un rappel de traitement correspondant aux informations d'état de maladie cibles (S103) ; recevoir des données de retour d'informations sur le traitement répondues par un côté utilisateur selon le rappel de traitement (S104) ; générer une étiquette de conclusion de diagnostic selon les données de retour d'informations sur le traitement et les informations d'état de maladie cibles (S105) ; et générer un rapport d'analyse d'état de maladie selon l'étiquette de conclusion de diagnostic (S106).
PCT/CN2022/087710 2021-08-30 2022-04-19 Procédé et appareil d'analyse d'état de maladie, dispositif électronique et support de stockage WO2023029506A1 (fr)

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