WO2021159759A1 - Procédé et appareil de structuration de dossier médical électronique, dispositif informatique et support d'enregistrement - Google Patents

Procédé et appareil de structuration de dossier médical électronique, dispositif informatique et support d'enregistrement Download PDF

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WO2021159759A1
WO2021159759A1 PCT/CN2020/125146 CN2020125146W WO2021159759A1 WO 2021159759 A1 WO2021159759 A1 WO 2021159759A1 CN 2020125146 W CN2020125146 W CN 2020125146W WO 2021159759 A1 WO2021159759 A1 WO 2021159759A1
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medical record
text
electronic medical
sentence
sub
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PCT/CN2020/125146
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Chinese (zh)
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周晓峰
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • 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
    • 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
    • G06N3/045—Combinations of 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
    • 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

Definitions

  • This application relates to the technical field of intelligent decision-making, and in particular to a method, device, computer equipment, and storage medium for structuring electronic medical records.
  • the medical record is the original record of the patient's diagnosis and treatment in the hospital. It contains the home page, the course record, the examination results, the doctor's order, the operation record, the nursing record and so on.
  • Electronic medical records not only refer to static medical record information, but also include related services provided.
  • Electronic medical records are information about individuals' life-long health status and medical care behaviors that are managed electronically, and involve all process information in the collection, storage, transmission, processing, and utilization of patient information.
  • the structuring of electronic medical records can efficiently extract the key information in the medical records by extracting the disease entities, drug entities, body parts entities, etc. from the electronic medical records through the neural network structure, effectively assisting doctors in core data analysis and data Search.
  • the invention realizes that the existing electronic medical records have different lengths.
  • the main purpose of this application is to provide an electronic medical record structuring method, device, computer equipment, and storage medium to solve the problem that the truncation of the electronic medical record affects the accuracy of the structure of the sentence around the truncation.
  • this application provides a method for structuring an electronic medical record, which includes the following steps:
  • the electronic medical record text is truncated to obtain multiple electronic medical record sub-texts
  • the sentence vector in each target medical record text is input into a classification model for calculation according to the order of the sentence corresponding to the sentence vector in the target medical record text to obtain a first output; wherein, the classification The model is based on two-way recurrent neural network model training;
  • the classification label of each sentence is obtained.
  • This application also provides an electronic medical record structuring device, including:
  • the first obtaining unit is used to obtain the electronic medical record text and the number of sentences in the electronic medical record text;
  • the detection unit is configured to detect whether the number of sentences in the electronic medical record text exceeds a preset threshold
  • the first truncation unit is used for truncating the electronic medical record text if it exceeds, to obtain multiple electronic medical record sub-texts;
  • the first introduction unit is used to introduce each of the electronic medical record sub-texts into the context through preset rules to obtain the target medical record text;
  • the first mapping unit is used to map each sentence in the target medical record text to a sentence vector of a fixed dimension
  • the first calculation unit is configured to input the sentence vector in each target medical record text into the classification model for calculation according to the sequence of the sentence corresponding to the sentence vector in the target medical record text to obtain the first Output; wherein, the classification model is based on a two-way recurrent neural network model training;
  • the second calculation unit is configured to obtain the classification label of each sentence according to the first output.
  • the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of a method for structuring an electronic medical record are implemented:
  • the electronic medical record text is truncated to obtain multiple electronic medical record sub-texts
  • the sentence vector in each target medical record text is input into a classification model for calculation according to the order of the sentence corresponding to the sentence vector in the target medical record text to obtain a first output; wherein, the classification The model is based on two-way recurrent neural network model training;
  • the classification label of each sentence is obtained.
  • This application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of an electronic medical record structuring method are realized:
  • the electronic medical record text is truncated to obtain multiple electronic medical record sub-texts
  • the sentence vector in each target medical record text is input into a classification model for calculation according to the order of the sentence corresponding to the sentence vector in the target medical record text to obtain a first output; wherein, the classification The model is based on two-way recurrent neural network model training;
  • the classification label of each sentence is obtained.
  • the electronic medical record structuring method, device, computer equipment and storage medium provided in this application introduce a part of the context at the truncation point according to preset rules, and input the introduced context and the truncated electronic medical record text into the classification model together.
  • the classification model Based on two-way cyclic neural network training, it can extract contextual information, and then calculate the classification of each sentence through SOFTMAX, which can effectively improve the structural accuracy of the sentence around the truncation.
  • FIG. 1 is a schematic diagram of the steps of a method for structuring an electronic medical record in an embodiment of the present application
  • FIG. 2 is a structural block diagram of an electronic medical record structuring device in an embodiment of the present application.
  • FIG. 3 is a schematic block diagram of the structure of a computer device according to an embodiment of the application.
  • an embodiment of the present application provides a method for structuring an electronic medical record, including:
  • Step S1 obtaining the electronic medical record text and the number of sentences in the electronic medical record text
  • Step S2 detecting whether the number of sentences in the electronic medical record text exceeds a preset threshold
  • Step S3 if it exceeds, the electronic medical record text is truncated to obtain multiple electronic medical record sub-texts;
  • Step S4 introducing each of the electronic medical record sub-texts into the context through preset rules to obtain the target medical record text;
  • Step S5 Map each sentence in the target medical record text to a sentence vector of a fixed dimension
  • Step S6 the sentence vector in each target medical record text is input into the classification model for calculation according to the order of the sentence corresponding to the sentence vector in the target medical record text to obtain a first output;
  • the classification model is based on training of a bidirectional cyclic neural network model;
  • Step S7 Obtain the classification label of each sentence according to the first output.
  • the electronic medical record text is acquired, and some preprocessing can be performed on the acquired electronic medical record text, such as text preprocessing and data cleaning through tools such as numpy, pandas, and jieba, including Chinese word segmentation, Remove stop words, remove useless symbols, etc., and desensitize the privacy in the electronic medical record text, and then remove patient privacy.
  • Privacy includes: name, bed number, hospital number, address and other key private information that can be easily identified by others , To obtain the number of sentences in the electronic medical record text after the above processing.
  • the context is introduced for each electronic medical record sub-text according to preset rules. For example, if one electronic medical record text is truncated into three electronic medical record sub-texts, they are as follows: The first electronic medical record sub-text, the second electronic medical record sub-text and the third electronic medical record sub-text, a part of the sentence in the second electronic medical record sub-text is introduced at the truncation of the first electronic medical record sub-text, in the second electronic medical record sub-text Introduce a part of the sentence of the first electronic medical record sub-text at the beginning of the truncation, introduce a part of the sentence of the third electronic medical record sub-text at the end of the second electronic medical record sub-text, and introduce the first sentence of the third electronic medical record sub-text at the truncation of the third electronic medical record sub-text 2. A part of the sentence in the sub-text of the electronic medical record.
  • each sentence in the target medical record text is mapped to a sentence vector of a fixed dimension.
  • the encoder can be passed through a neural network (convolutional neural network, recurrent neural network, transormer, etc.) (Encoder), by mapping the sentence to a vector of fixed dimensions, we can get the vector representation of a single sentence through the neural network.
  • a neural network convolutional neural network, recurrent neural network, transormer, etc.
  • the sentences in the medical record are not independent of each other but context-related.
  • the part describing the treatment process is usually composed of multiple sentences, and the context of a sentence describing the treatment is also the probability of describing the treatment process It is more likely than describing the user’s past medical history. Therefore, only a single sentence classification of the text will not achieve good results. It is necessary to include all the context information and input the sentence vectors into the classification model in order.
  • This classification The model is trained based on a two-way cyclic neural network model. After the forward and backward calculations of the classification model, each sentence can better obtain contextual information and effectively improve the accuracy of classification.
  • the classification model can Each sentence is classified into one of basic information, personal history, family history, past history, current medical history, chief complaint, examination, diagnosis, treatment, summary, and others.
  • the classification label of each sentence is obtained according to the first output.
  • the first output of each sentence vector is calculated by SOFTMAX, and SOFTMAX can map a K-dimensional arbitrary real number vector into Another K-dimensional real number vector, where each element in the vector has a value between (0, 1).
  • the function expression of SOFTMAX is: Among them, K represents the number of categories, j represents a category in K categories, j ⁇ (0, K], z j represents the value of the category.
  • a part of the context is introduced at the truncation according to the preset rules, and the introduced context and the truncated electronic medical record text are input together into the classification model to obtain the first output.
  • the classification model is based on a two-way cyclic neural network training.
  • the context information can be extracted, and the classification label of each sentence can be obtained according to the first output, which can effectively improve the structural accuracy of the sentence at the truncation of the electronic medical record.
  • step S7 of the step of obtaining the classification label of each sentence according to the first output includes:
  • Step S71 Input the first output of each sentence vector into a CRF (conditional random field, conditional random field) network and/or a self-attention network to obtain a second output;
  • CRF condition random field, conditional random field
  • step S72 the second output of each sentence vector is calculated by SOFTMAX to obtain the classification label of each sentence.
  • inputting the first output into the CRF network and/or the self-attention network can further improve the influence of the context information of the classification model and strengthen the contextual connection between sentences.
  • the SOFTMAX calculation can be performed between the first output to obtain the classification label of each sentence.
  • the step S5 of mapping each sentence in the target medical record text to a sentence vector of a fixed dimension includes:
  • Step S51 input each sentence in the target medical record text into the neural network
  • Step S52 Map each sentence to a sentence vector of a fixed dimension through the encoder of the neural network.
  • a sentence is mapped to a vector of a fixed dimension, and we can obtain a vector representation of a single sentence through the neural network.
  • a neural network which may be a convolutional neural network, a cyclic neural network, a transformer, etc.
  • a sentence is mapped to a vector of a fixed dimension, and we can obtain a vector representation of a single sentence through the neural network.
  • the transformer model as an example.
  • the first sub-layer is the multi-head attention layer.
  • the second is a simple fully connected layer. A residual connection is used between each sub-layer layer.
  • the step S4 of introducing each of the electronic medical record sub-texts into the context through preset rules to obtain the target medical record text includes:
  • Step S41 detecting the position of each sub-text of the electronic medical record in the text of the electronic medical record
  • Step S42 when the electronic medical record sub-text starts at the position of the electronic medical record text, introduce a preset number of sentences at the beginning of the next electronic medical record sub-text at the truncation of the electronic medical record sub-text;
  • Step S43 When the electronic medical record text is in the middle of the position of the electronic medical record text, introduce a preset number of sentences at the end of the last electronic medical record sub-text at the beginning of the electronic medical record sub-text.
  • the ending truncation of the electronic medical record sub-text introduces a preset number of sentences at the beginning of the next electronic medical record sub-text;
  • Step S44 when the electronic medical record sub-text ends at the position of the electronic medical record text, introduce a preset number of sentences at the end of the last electronic medical record sub-text at the truncation of the electronic medical record sub-text.
  • a part of sentences is introduced for each electronic medical record sub-text.
  • the number of sentences in an electronic medical record text is 120, and the classification model can only support 50 sentences at a time, and the electronic self-medical record sub-texts can be evened according to the number of sentences.
  • the end of the first part can be introduced into the first 10 sentences at the beginning of the second part to form the first target medical record text; the end of the first part can be introduced at the beginning of the second part At the end of the second part, the first 10 sentences at the beginning of the third part are introduced to form the second target medical record sub-file.
  • the specific number of sentences introduced in each electronic medical record sub-text can be set in advance according to needs. Certainly.
  • a sentence in the context is introduced for each electronic medical record sub-text, and then input to the classification model for classification, and the accuracy of classification of each sentence is improved through the connection between the contexts.
  • the method includes:
  • Step S2A if it does not exceed, map each sentence in the electronic medical record text to a sentence vector of a fixed dimension;
  • Step S2B input the sentence vectors in each electronic medical record text into the classification model in order for calculation to obtain a third output;
  • step S2C the third output of each sentence vector is calculated by SOFTMAX to obtain the classification label of each sentence.
  • the sentence vector of each sentence is directly input into the classification model in order for calculation, and then the classification label of each sentence is calculated by the SOFTMAX function.
  • the sentence vector in each target medical record text is input into the classification model for calculation according to the order of the sentence corresponding to the sentence vector in the target medical record text to obtain the first Before step S6 of an output, it includes:
  • Step S6a Obtain case samples in the training data set, and each sentence in the medical record sample has a correct classification label
  • Step S6b truncating the medical record sample to obtain multiple medical record sub-samples
  • step S6c each of the medical record sub-samples is introduced into the context through a preset rule to obtain a target medical record sample
  • Step S6d mapping each sentence in the target medical record sample to a sentence vector of a fixed dimension
  • Step S6e input the sentence vectors in each target medical record text into the bidirectional recurrent neural network model in order for calculation to obtain training output;
  • Step S6f calculating the training output through SOFTMAX to obtain the predicted output
  • Step S6g Calculate the loss value of each sentence in the medical record sub-sample by using a loss function
  • step S6h the classification model parameters are determined according to the loss value, and the training of the classification model is completed.
  • the loss value of each sentence in the medical record subsample is calculated.
  • the medical record subsample introduces context according to certain rules, and the context follows each sentence in the medical record subsample and is input to the bidirectional recurrent neural network. , Extract the context information, and get the output of each sentence.
  • the output of each sentence is calculated through SOFTMAX to obtain the expected output of each sentence, and then only the loss value of each sentence in the medical record subsample is calculated through the loss function, and the smallest loss value is selected
  • the corresponding model parameters are used as the final model parameters to complete the training of the classification model.
  • each medical record sub-sample introduces context, but the introduced context only provides context information, and does not participate in the calculation of the loss value and the final classification. Specifically, through the cross entropy function Calculate the loss value of each sentence in the medical record subsample, where y is the expected output of each sentence in the medical record subsample, For its correct classification label.
  • the electronic medical record structuring method provided in this application can be used in the blockchain field.
  • the trained classification model is stored in the blockchain network.
  • the electronic medical record text can also be stored in the blockchain network.
  • the blockchain is New application modes of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
  • Blockchain Network (Blockchain Network) refers to the collection of a series of nodes that incorporate new blocks into the blockchain through consensus.
  • the underlying platform of the blockchain can include processing modules such as user management, basic services, smart contracts, and operation monitoring.
  • the user management module is responsible for the identity information management of all blockchain participants, including the maintenance of public and private key generation (account management), key management, and maintenance of the correspondence between the user’s real identity and the blockchain address (authority management), etc.
  • authorization supervise and audit certain real-identity transactions, and provide risk control rule configuration (risk control audit); basic service modules are deployed on all blockchain node devices to verify the validity of business requests, After completing the consensus on the valid request, it is recorded on the storage.
  • the basic service For a new business request, the basic service first performs interface adaptation analysis and authentication processing (interface adaptation), and then encrypts the business information through the consensus algorithm (consensus management), After encryption, it is completely and consistently transmitted to the shared ledger (network communication), and recorded and stored; the smart contract module is responsible for contract registration and issuance, contract triggering and contract execution.
  • interface adaptation interface adaptation
  • consensus algorithm consensus algorithm
  • the smart contract module is responsible for contract registration and issuance, contract triggering and contract execution.
  • the operation monitoring module is mainly responsible for the deployment of the product release process , Configuration modification, contract settings, cloud adaptation, and visual output of real-time status during product operation, such as: alarms, monitoring network conditions, monitoring node equipment health status, etc.
  • the structuring method, device, computer equipment, and storage medium of electronic medical records provided in this application can be applied in the field of smart medical care to accelerate the construction of digital medical care, thereby promoting the construction of smart cities.
  • an embodiment of the present application further provides an electronic medical record structuring device, including:
  • the first obtaining unit 10 is configured to obtain the electronic medical record text and the number of sentences in the electronic medical record text;
  • the detection unit 20 is configured to detect whether the number of sentences in the electronic medical record text exceeds a preset threshold
  • the first truncation unit 30 is used for truncating the electronic medical record text if it exceeds, to obtain a plurality of electronic medical record sub-texts;
  • the first introduction unit 40 is configured to introduce each of the electronic medical record sub-texts into the context through preset rules to obtain the target medical record text;
  • the first mapping unit 50 is configured to map each sentence in the target medical record text to a sentence vector of a fixed dimension
  • the first calculation unit 60 is configured to input the sentence vector in each target medical record text into the classification model for calculation according to the order of the sentence corresponding to the sentence vector in the target medical record text to obtain the first One output; wherein, the classification model is based on a two-way recurrent neural network model training;
  • the second calculation unit 70 is configured to obtain the classification label of each sentence according to the first output.
  • the second calculation unit 70 includes:
  • a first input subunit configured to input the first output of each sentence vector into a CRF network and/or a self-attention network to obtain a second output;
  • the calculation subunit is configured to perform SOFTMAX calculation on the second output of each sentence vector to obtain the classification label of each sentence.
  • the first mapping unit 50 includes:
  • the second input subunit is used to input each sentence in the target medical record text into the neural network
  • the mapping subunit is used to map each sentence to a sentence vector of a fixed dimension through the encoder of the neural network.
  • the first introduction unit 40 includes:
  • the detection subunit is used to detect the position of each of the electronic medical record sub-texts in the electronic medical record text
  • the first introduction sub-unit is used for when the electronic medical record sub-text starts at the position of the electronic medical record text, introduce the preset of the beginning part of the next electronic medical record sub-text at the truncation of the electronic medical record sub-text Number of sentences
  • the second introduction sub-unit is used to introduce a preset end portion of the previous electronic medical record sub-text at the beginning and truncation of the electronic medical record sub-text when the electronic medical record text is in the middle of the position of the electronic medical record text Number of sentences, introducing a preset number of sentences at the beginning of the next electronic medical record sub-text at the end truncation of the electronic medical record sub-text;
  • the third introduction sub-unit is used to introduce the preset ending part of the last electronic medical record sub-text at the truncation of the electronic medical record sub-text when the electronic medical record sub-text ends at the position of the electronic medical record text Number of sentences.
  • the electronic medical record structuring device further includes:
  • the second mapping unit is used to map each sentence in the electronic medical record text to a sentence vector of a fixed dimension if it is not exceeded;
  • the third calculation unit is configured to input the sentence vectors in each electronic medical record text into the classification model in order for calculation to obtain a third output;
  • the fourth calculation unit is configured to perform SOFTMAX calculation on the third output of each sentence vector to obtain the classification label of each sentence.
  • the electronic medical record structuring device further includes:
  • the second acquiring unit is used to acquire case samples in the training data set, where each sentence in the medical record sample has a correct classification label
  • the second truncation unit is used for truncating the medical record sample to obtain multiple medical record sub-samples
  • the second introduction unit is used to introduce each of the medical record sub-samples into the context through preset rules to obtain the target medical record sample;
  • the third mapping unit is used to map each sentence in the target medical record sample to a sentence vector of a fixed dimension
  • a fifth calculation unit configured to sequentially input the sentence vectors in each target medical record text into the bidirectional cyclic neural network model for calculation to obtain training output;
  • the sixth calculation unit is used to calculate the training output through SOFTMAX to obtain the predicted output
  • the seventh calculation unit is used to calculate the loss value of each sentence in the medical record sub-sample by using a loss function
  • the determining unit is used to determine the parameters of the classification model according to the loss value to complete the training of the classification model.
  • the seventh calculation unit includes:
  • the calculation subunit is used to calculate the loss value of each sentence in the medical record subsample through a cross entropy function; the formula of the cross entropy function is: The y is the predicted output, For the correct classification label.
  • an embodiment of the present application also provides a computer device.
  • the computer device may be a server, and its internal structure may be as shown in FIG. 3.
  • the computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor designed by the computer is used to provide calculation and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the database of the computer equipment is used to store electronic medical record data and so on.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer program is executed by the processor to realize a structuring method of electronic medical records.
  • FIG. 3 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • An embodiment of the present application also provides a computer-readable storage medium.
  • the above-mentioned storage medium may be a non-volatile storage medium or a volatile storage medium.
  • a computer program is stored thereon, and when the computer program is executed by a processor, a method for structuring an electronic medical record is realized.
  • the electronic medical record structuring method, device, computer equipment, and storage medium provided in the embodiments of this application introduce a part of the context at the truncation place according to preset rules, and combine the introduced context and the truncated electronic medical record text Enter the classification model together.
  • the classification model is based on two-way recurrent neural network training, which can extract context information, and then calculate the classification of each sentence through SOFTMAX, which can effectively improve the accuracy of the structure of the sentence around the truncation.
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

L'invention concerne un procédé et un appareil de structuration de dossier médical électronique, un dispositif informatique et un support d'enregistrement, se rapportant au domaine de l'intelligence artificielle et destinés à être utilisés dans le domaine de la médecine intelligente. Le procédé consiste : à acquérir un texte de dossier médical électronique et le nombre de phrases dans le texte de dossier médical électronique (S1) ; à détecter si le nombre de phrases dans le texte de dossier médical électronique dépasse un seuil prédéfini (S2) ; si le nombre de phrases dépasse ce seuil, à tronquer le texte de dossier médical électronique pour obtenir une pluralité de sous-textes de dossier médical électronique (S3) ; à incorporer chaque sous-texte de dossier médical électronique dans des textes précédents et suivants au moyen d'une règle prédéfinie pour obtenir un texte de dossier médical cible (S4) ; à mapper chaque phrase dans le texte de dossier médical cible en tant que vecteur de phrase à dimension fixe (S5) ; à entrer chaque vecteur de phrase du texte de dossier médical cible de manière séquentielle dans un modèle de classification pour le calcul afin d'obtenir une première sortie, le modèle de classification étant construit sur la base d'un entraînement de réseau neuronal récurrent bidirectionnel (S6) ; sur la base de la première sortie, à obtenir une étiquette de classification pour chaque phrase (S7). La présente invention permet d'améliorer la précision de la structuration de phrases au niveau de sites de troncature.
PCT/CN2020/125146 2020-09-04 2020-10-30 Procédé et appareil de structuration de dossier médical électronique, dispositif informatique et support d'enregistrement WO2021159759A1 (fr)

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CN202010922768.XA CN112016279B (zh) 2020-09-04 2020-09-04 电子病历结构化方法、装置、计算机设备和存储介质
CN202010922768.X 2020-09-04

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