Entity recognition system for breast electronic medical records based on multi-criteria active learning

Abstract

The invention relates to a breast electronic medical record entity recognition system based on multi-standard active learning, which is characterized in that it comprises: a preprocessing module; an entity recognition module; and an active learning module. The present invention considers three aspects of labeling data volume, sentence labeling cost, and data sampling balance, and designs an active learning selection strategy for text sequence labeling to reduce the total labeling workload. On the one hand, the present invention can be used to build systems such as identification marks for breast disease risk patients, disease drug recommendation, decision-making assistance, etc., to help doctors improve the implementation efficiency of standardized diagnosis and treatment of breast diseases, and to provide scientific basis and suggestions; on the other hand, it can also assist Doctors discover potential abnormalities in the diagnosis and treatment process, reduce misdiagnosis and missed diagnosis rates, and improve the cure rate of patients with breast diseases, which is of great value to the intelligent development of breast disease research.

Description

technical field [0001] The invention relates to the field of medical natural language processing, in particular to a breast electronic medical record entity recognition system based on multi-standard active learning. 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 reflect detailed health informat...

AI Technical Summary

Problems solved by technology

Since electronic medical records belong to text data in a specific professional field, its corpus annotation not only takes a lot of time, but also requires manpower with strong medical expertise, and it is difficult to obtain a large amount of annotated clinical medical data.

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