WO2020074023A1 - Procédé et dispositif basés sur l'apprentissage profond destinés au criblage de phrases clés dans un document médical - Google Patents

Procédé et dispositif basés sur l'apprentissage profond destinés au criblage de phrases clés dans un document médical Download PDF

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WO2020074023A1
WO2020074023A1 PCT/CN2019/124561 CN2019124561W WO2020074023A1 WO 2020074023 A1 WO2020074023 A1 WO 2020074023A1 CN 2019124561 W CN2019124561 W CN 2019124561W WO 2020074023 A1 WO2020074023 A1 WO 2020074023A1
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processed
medical
clauses
segmentation
word
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PCT/CN2019/124561
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English (en)
Chinese (zh)
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赵荣生
宋再伟
黄振城
王则远
周旻
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北京大学第三医院
北京诺道认知医学科技有限公司
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Publication of WO2020074023A1 publication Critical patent/WO2020074023A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

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  • Embodiments of the present disclosure relate to the field of computers, and in particular to a method and device for screening critical sentences in medical literature based on deep learning.
  • the main content of the text is often covered in a set of important key sentences. These key sentences can clearly express the content characteristics of the text (such as domain categories, theme ideas, central meaning, etc.). It is based on this understanding.
  • the identification and screening of key sentences that can represent the main content of the text is a very important step. It is important for the disclosure of subject literature and the reflection of the knowledge hidden in the text. Meaning.
  • the key sentence screening is simply to identify and extract sentences containing useful information according to certain purpose requirements, so as to condense the text and obtain rich information from a small amount of data.
  • Traditional key sentence screening methods are generally based on statistical methods, using statistical information such as location and frequency to find the sentence that best represents the subject of the article as the key sentence.
  • the structure of the article it can be divided into unstructured screening analysis type and structured screening analysis type.
  • the former calculates the weight of the sentence of the article, and finds the sentence with the highest weight as the key sentence.
  • the latter first analyzes the semantic structure of the article to find out the topic structure of the article, and then extracts sentences from each topic to form a key sentence.
  • the statistical method of filtering based on structure or weight is easy to ignore the content of the sentence itself in actual operation, and the key sentences that are distributed in the text but contain the content of the subject words are filtered out, and the redundancy is greater.
  • the widely used deep learning algorithm focuses on the content of the sentence itself, and automatically learns the sample features by simulating the structure of the neural network of the human brain, so as to filter out key sentences containing key information and prepare for further analysis.
  • the algorithm has so far been limited to analyzing isolated sentences.
  • the contextual relationship between sentences and sentences has not been systematically studied in terms of the constraints and effects of this sentence.
  • embodiments of the present disclosure provide a method and device for screening key sentences in medical literature based on deep learning.
  • an embodiment of the present disclosure proposes a method for screening key sentences in medical literature based on deep learning, including:
  • an embodiment of the present disclosure proposes a device for screening critical sentences in medical literature based on deep learning, including:
  • the generating unit is used for segmenting the medical document to be processed, segmenting the segmentation, and generating the word vector of the segmentation by encoding the segmentation in the order in which the segmentation appears in the medical document to be processed;
  • the input unit is configured to input the word vectors of the clauses into a pre-trained deep learning-based convolutional neural network model to obtain key sentences in the medical literature to be processed.
  • an embodiment of the present disclosure provides an electronic device, including: a processor, a memory, a bus, and a computer program stored on the memory and executable on the processor;
  • processor and the memory communicate with each other through the bus;
  • an embodiment of the present disclosure provides a non-transitory computer-readable storage medium, where a computer program is stored on the storage medium, and the computer program is executed by a processor to implement the above method.
  • the method and device for screening critical sentences in medical literature based on deep learning utilizes the trained deep learning-based convolutional neural network model to screen critical sentences in medical literature, because the constructed convolutional neural network model can Combined with context semantics, the local relevance of the document is captured, so that the scheme can improve the accuracy of screening key sentences in medical literature compared to the existing technology.
  • FIG. 1 is a schematic flowchart of an embodiment of a method for screening key sentences in medical literature based on deep learning of the present disclosure
  • FIG. 2 is a schematic structural diagram of an embodiment of a key sentence selection device in medical literature based on deep learning of the present disclosure
  • FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
  • this embodiment discloses a method for selecting key sentences in medical literature based on deep learning, including:
  • the method for screening critical sentences in medical literature based on deep learning uses a trained deep learning-based convolutional neural network model to screen critical sentences in medical literature, because the constructed convolutional neural network model can combine context Semantics captures the local relevance of documents, so that this solution can improve the accuracy of screening key sentences in medical literature compared to existing technologies.
  • the convolutional layer of the convolutional neural network model uses a multivariate filter window, each window corresponds to a first number of filters, and the independent variable of the convolution operation is the word segmentation vector in the corresponding filter window
  • the pooling layer of the convolutional neural network model adopts the Max-over-time-pooling method.
  • the convolutional layer of the model uses multi-filter windows with widths of 3, 4, and 5, each window corresponds to 100 filters, and slides different windows to traverse each participle in the clause.
  • each filter The device can get a feature map set.
  • the calculation formula of the feature set is as follows:
  • the pooling layer of the model adopts the Max-over-time-pooling method. For the feature set generated by different filters in each filter window, the maximum value of the set is taken as an important representative feature. In this way, the features of different sliding window sizes become a fixed length, which are spliced together to form a feature vector of 3 * 100 length.
  • the last layer of the model is a fully connected softmax layer, which outputs the probability of each category. Through multiple rounds of iterative training and parameter adjustment, the optimal model parameters are found.
  • the model output needs to be constructed for the training data.
  • the specific method is: according to the PICO index matrix, if a clause does not contain any matrix elements, it means that the clause does not contain relevant fields worth studying. Key information, so the target value of the clause is set to 0. If the clause contains one or more elements of the matrix, it means that the clause may contain important information. In order to avoid missing key information, it needs to be filtered out for subsequent In-depth study, so the target value of the clause is set to 1.
  • the medical documents to be processed are segmented, and the segmentation is segmented, including:
  • the medical documents to be processed are segmented according to punctuation marks, and the segmentation is segmented based on the segmentation algorithm and the medical lexicon.
  • the word vectors of the clauses are generated by identifying and encoding the tokens in the order in which the tokens appear in the medical document to be processed, including:
  • the participles of the clauses are identified and coded, and the segmented and segmented tokens after the identification coding are filled with zeros to expand, so that the number of elements of the expanded clauses with zero-filling is the most Long clauses contain the same number of tokens, and the zero-filled clauses are used as the word vectors of the corresponding clauses.
  • the tokens of the clauses are identified and encoded (id encoding) according to the order in which they appear in the document.
  • the starting value of the encoding is 1 and the ending value is the vocabulary of the document size.
  • max_sentence_len the number of the most participles in all clauses as max_sentence_len, and then add 0 to expand the id-encoded clauses to make the length reach max_sentence_len, which is the word vector of the clause, where the number of 0s in the word vector is equal to max_sentence_len-Number of words.
  • this embodiment discloses a device for screening critical sentences in medical literature based on deep learning, including:
  • the generating unit 1 is used for segmenting the medical document to be processed, segmenting the segmentation, and generating the word vector of the segmentation by encoding the segmentation according to the order in which the segmentation appears in the medical document to be processed;
  • the input unit 2 is configured to input the word vectors of the clauses into a pre-trained convolutional neural network model based on deep learning to obtain key sentences in the medical literature to be processed.
  • the generating unit 1 performs sentence segmentation on the medical document to be processed, performs word segmentation on the sentence segment, and generates the word vector of the sentence segment by encoding the word segmentation in the order in which the word segment appears in the medical document to be processed
  • the input unit 2 inputs the word vector of the clause into a pre-trained convolutional neural network model based on deep learning to obtain the key sentence in the medical literature to be processed.
  • the key sentence screening device in the medical literature based on deep learning uses the trained deep learning-based convolutional neural network model to screen key sentences in the medical literature, because the constructed convolutional neural network model can combine context Semantics captures the local relevance of documents, so that this solution can improve the accuracy of screening key sentences in medical literature compared to existing technologies.
  • the convolutional layer of the convolutional neural network model uses a multivariate filter window, each window corresponds to a first number of filters, and the independent variable of the convolution operation is the word segmentation vector in the corresponding filter window
  • the pooling layer of the convolutional neural network model adopts the Max-over-time-pooling method.
  • the generating unit is specifically used to:
  • the medical documents to be processed are segmented according to punctuation marks, and the segmentation is segmented based on the segmentation algorithm and the medical lexicon.
  • the generating unit is specifically used to:
  • the participles of the clauses are identified and coded, and the segmented and segmented tokens after the identification coding are filled with zeros to expand, so that the number of elements of the expanded clauses with zero-filling and Long clauses contain the same number of tokens, and the zero-filled clauses are used as the word vectors of the corresponding clauses.
  • the key sentence selection device in the medical literature based on deep learning of this embodiment may be used to execute the technical solutions of the foregoing method embodiments, and its implementation principles and technical effects are similar, and will not be repeated here.
  • FIG. 3 shows a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present disclosure.
  • the electronic device may include: a processor 11, a memory 12, a bus 13, and stored on the memory 12 and may be A computer program running on the processor 11;
  • processor 11 and the memory 12 communicate with each other through the bus 13;
  • the method provided by each of the above method embodiments is implemented, for example, including: segmenting a medical document to be processed, segmenting a clause, and performing a word segmentation on the medical subject to be processed according to the segmentation
  • the order of occurrence in the literature is to encode the word segmentation to generate the word vector of the sentence; input the word vector of the sentence into a pre-trained convolutional neural network model based on deep learning to obtain the medical literature to be processed Key sentence in
  • Embodiments of the present disclosure provide a non-transitory computer-readable storage medium on which a computer program is stored.
  • the method provided by the foregoing method embodiments is implemented, for example, including: medical documents to be processed Perform clause segmentation, segment the clauses, and generate the word vectors of the clauses by coding the tokens in the order in which they appear in the medical literature to be processed; input the word vectors of the clauses in advance
  • key sentences in the medical literature to be processed are obtained.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.
  • computer usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, the instructions The device implements the functions specified in one block or multiple blocks of the flowchart one flow or multiple flows and / or block diagrams.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are performed on the computer or other programmable device to produce computer-implemented processing, which is executed on the computer or other programmable device
  • the instructions provide steps for implementing the functions specified in one block or multiple blocks of the flowchart one flow or multiple flows and / or block diagrams.
  • the terms “installation”, “connected”, and “connection” should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, It can also be an electrical connection; it can be directly connected, or it can be indirectly connected through an intermediary, or it can be a connection between two components.

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Abstract

Selon certains modes de réalisation, la présente invention concerne un procédé et un dispositif basés sur un apprentissage profond destinés au criblage de phrases clés dans un document médical, capables d'améliorer la précision de criblage de phrases clés dans des documents médicaux. Le procédé comprend les étapes suivantes : S1, effectuer une segmentation de phrase sur un document médical à traiter, effectuer une segmentation de mot sur les phrases de composant, étiqueter et coder les mots de composant selon l'ordre d'apparition des mots de composant dans le document médical à traiter, et générer des vecteurs de mots pour les phrases de composant ; S2, entrer les vecteurs de mots des phrases de composant dans un modèle de réseau neuronal convolutif basé sur un apprentissage profond pré-appris, et obtenir les phrases clés dans le document médical à traiter.
PCT/CN2019/124561 2018-10-12 2019-12-11 Procédé et dispositif basés sur l'apprentissage profond destinés au criblage de phrases clés dans un document médical WO2020074023A1 (fr)

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