Electronic medical record processing method, model training method and related devices
A network model and training sample technology, applied in neural learning methods, biological neural network models, special data processing applications, etc., can solve the problems of low sequence labeling accuracy, difficulty in learning the deep semantic meaning of sequences, etc., to improve accuracy Effect
Active Publication Date: 2019-11-08
NEW H3C BIG DATA TECH CO LTD
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[0003] However, in the schemes aimed at solving sequence labeling tasks, the accuracy of sequence labeling is often low due to the difficulty of learning the deep semantic meaning of the sequence
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[0041] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.
[0042] Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art w...
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The invention provides an electronic medical record processing method, a model training method and related devices. The invention relates to the technical field of natural language processing. The method comprises the following steps: constructing a semantic connection network based on a convolution algorithm, an Attention mechanism and a feedforward neural network algorithm; processing the training sample sequence by using the semantic connection network; learning deep semantic information of the training sample sequence; taking the obtained semantic annotation sequence as the input of a second feedforward neural network; obtaining an initial prediction result corresponding to the training sample sequence; updating the initial prediction result based on a probability transfer mechanism toobtain a more accurate updating prediction result; updating model parameters of the sequence labeling network model based on the updated prediction result and the training labeling result corresponding to the training sample sequence. Compared with the prior art, the sequence labeling network model can fully learn deep semantic information and long-distance feature information of the sample sequence, and the accuracy of sequence labeling can be improved.
Description
technical field [0001] The present application relates to the technical field of natural language processing, in particular, to an electronic medical record processing method, a model training method and related devices. Background technique [0002] Sequence tagging tasks are an important type of tasks in Natural Language Processing (NLP), especially in natural language sequences, time series and other tasks, such as word segmentation tasks, entity recognition tasks, time series tasks, part-of-speech tagging Tasks, etc., can be classified as the application scenarios of sequence labeling tasks. [0003] However, in the schemes aimed at solving sequence labeling tasks, the accuracy of sequence labeling is often low due to the difficulty of learning the deep semantic meaning of the sequence. Contents of the invention [0004] The purpose of this application is to provide an electronic medical record processing method, a model training method and related devices, which can ...
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IPC IPC(8): G06F16/36G06N3/04G06N3/08G16H50/70G16H70/20
CPCG06F16/367G06N3/08G16H50/70G16H70/20G06N3/045
Inventor 王李鹏
Owner NEW H3C BIG DATA TECH CO LTD