Mammary gland electronic medical record entity recognition system based on multi-standard active learning

Abstract

The invention relates to a mammary gland electronic medical record entity recognition system based on multi-standard active learning, and the system is characterized in that the system comprises a preprocessing module; an entity identification module; and an active learning module. According to the invention, the active learning selection strategy for text sequence annotation is designed by considering three aspects of annotation data volume, sentence annotation cost and data sampling balance, so the total annotation workload is reduced. On the one hand, the system can be used for constructingsystems such as breast disease risk patient identification marks, disease medicine recommendation and auxiliary decision diagnosis, doctors are helped to improve the execution efficiency of breast disease standardized diagnosis and treatment, and scientific bases and suggested schemes are provided; on the other hand, doctors can be assisted in finding out potential abnormal conditions in the diagnosis and treatment process, the misdiagnosis and missed diagnosis rate is reduced, the curing probability of breast disease patients is increased, and important value is achieved for 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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