Medical named entity recognition model training method, recognition method and federated learning system

A technology of named entity recognition and model training, which is applied in neural learning methods, character and pattern recognition, and biological neural network models. Risk of Data Leakage, Effects of Addressing Data Privacy

Active Publication Date: 2022-05-17
北京智源人工智能研究院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of low medical data sharing and model learning that do not consider the localization of medical vocabulary descriptions in the prior art, the present invention provides the following technical solutions

Method used

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  • Medical named entity recognition model training method, recognition method and federated learning system
  • Medical named entity recognition model training method, recognition method and federated learning system
  • Medical named entity recognition model training method, recognition method and federated learning system

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Experimental program
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Embodiment 1

[0058] like figure 1 As shown, the first aspect of the present invention provides a medical named entity recognition model training method. The method can be executed by a medical institution node, and the medical institution node can refer to an institution that performs model training based on a distributed server in the federated learning system, or can also refer to the distributed server itself in the federated learning system, including:

[0059] S101. Receive a global model for identifying a medical named entity sent by a central server.

[0060] An example of the overall federated learning framework of the present invention is figure 2 shown, including central server and distributed server. The distributed server corresponds to the medical named entity recognition system distributed in various medical institutions, and is used for model training and prediction locally, while the central server is responsible for initial model training, model issuance, gradient recov...

Embodiment 2

[0083] like Figure 4 As shown, the second aspect of the present invention provides a medical named entity recognition model training method. The method can be executed by the central server, and specifically includes:

[0084] S201. Distribute the global model for identifying medical named entities to each medical institution node in the federated learning system, so that each medical institution node trains the global model based on the respective local medical text annotation data, and calculates to obtain corresponding gradient data.

[0085] S202. Receive the gradient data respectively sent by each of the medical institution nodes, and train the global model based on each of the gradient data to obtain a new global model.

[0086] S203. If the new global model is currently converged, distribute the converged global model to each of the medical institution nodes in the federated learning system, so that each of the medical institution nodes is based on the corresponding ...

Embodiment 3

[0096] like Figure 5 As shown, the present invention provides, in a third aspect, a method for training a localized medical named entity recognition model. The method is executed by a medical institution node, and specifically includes:

[0097] S301. Obtain medical data.

[0098] In S301, the medical institution node can perform data cleaning, invalid data screening, and data format normalization processing on the medical data based on preset preprocessing rules, so as to further improve the execution efficiency and accuracy of S302.

[0099] S302. Input the medical data into a localized medical named entity recognition model, so that the localized medical named entity recognition model outputs a medical named entity recognition result corresponding to the medical data.

[0100] Wherein, the localized medical named entity recognition model is obtained in advance based on the medical named entity recognition model training method described in the first aspect.

[0101] Afte...

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PUM

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Abstract

The invention discloses a medical named entity recognition model training method, a medical named entity recognition method and a federated learning system. The medical named entity recognition model training method includes: receiving the global model sent by the central server for identifying medical named entities; training the global model based on local medical text annotation data, and calculating corresponding gradient data; sending the gradient data to the central server , so that the central server trains the global model based on each gradient data received by the federated learning system to obtain a new global model. If the new global model is currently converged, distribute the converged global model; receive the converged global model ; Perform localized fine-tuning on the converged global model based on the local prompt template to form a localized medical named entity recognition model. The technical solution of the invention realizes the privacy protection of medical data and the local personalization of the medical named entity recognition model.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a medical named entity recognition model training method, a medical named entity recognition method and a federated learning system. Background technique [0002] Named Entity Recognition (MNER) in the medical field is the basis for the construction of medical knowledge graphs and medical big data. It is of great value for applications such as medical intelligence and auxiliary diagnosis. [0003] Existing medical named entity recognition technologies usually rely on large-scale labeled data. However, due to the privacy of medical data, it is difficult and costly to obtain a large amount of medical named entity labeled data. Currently, it is possible to produce high-quality labeled medical professionals. Very scarce; and there may be data privacy risks in the data transfer process, so medical institutions do not want to transmit data to external servers to sh...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/295G06F40/186G06K9/62G06N3/08
CPCG06F40/295G06F40/186G06N3/08G06F18/214
Inventor 安波
Owner 北京智源人工智能研究院
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