Mammary gland medical record entity recognition labeling enhancement system based on multi-agent reinforcement learning

Abstract

The invention discloses a mammary gland medical record entity recognition labeling enhancement system based on multi-agent reinforcement learning. The system comprises a mammary gland clinical electronic medical record data preprocessing module used for processing original data into a representation form which can be identified and analyzed by a system and analyzing the mammary gland clinical electronic medical record data in terms of medical record contents, structural features, language features and semantic features; a medical clinical entity recognition module used for extracting medical concept entities in texts; and a reinforcement learning labeling enhancement module used for correcting wrong entity labels extracted from mammary gland electronic medical records. According to the method, the multi-agent reinforcement learning model for entity recognition sequence labeling is designed based on the partially observable Markov decision process, the labeling result is corrected, andcompared with a traditional deep learning entity recognition model, the accuracy is effectively improved.

Description

technical field [0001] The invention relates to a breast medical record entity recognition and labeling enhancement system based on multi-agent reinforcement learning, which belongs to the technical field of medical natural language processing. Background technique [0002] With the popularization and development of hospital information technology, a comprehensive information system with electronic medical record system as the core and effective integration of multiple clinical information systems has been gradually formed. During the decades of use of the electronic medical record system, a large amount of medical text data has been accumulated, and many institutions and teams have emerged to conduct a lot of research on the medical text structure. [0003] Electronic medical records are important clinical information resources closely related to medicine and health generated during medical activities. They not only contain rich medical professional knowledge, but also refl...

AI Technical Summary

Problems solved by technology

Due to the structural characteristics and data distribution problems of the neural network itself, there is an upper limit in the model recognition performance, which cannot meet the high accuracy requirements of the model output results in clinical medical applications

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