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Named entity identification method for medical text data

A named entity recognition and text data technology, applied in the field of information extraction, can solve the problems of medical named entity recognition of medical text data, etc.

Inactive Publication Date: 2017-09-15
BEIJING UNIV OF CHEM TECH
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, the complexity of Chinese natural language processing and the uniqueness of the above-mentioned medical text data make medical named entity recognition a difficult problem.

Method used

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  • Named entity identification method for medical text data
  • Named entity identification method for medical text data
  • Named entity identification method for medical text data

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Embodiment

[0024] In this embodiment, a Hidden Markov Model (HMM) is used to sequentially label the original medical text to obtain a prediction word segmentation result. After the predicted word segmentation process is completed, the semi-supervised learning method is used to iteratively self-learn the word segmentation results to obtain accurate word segmentation and named entity recognition results. In this embodiment, by comparing the advantages and disadvantages of various supervised learning methods and combining the semi-supervised learning method for error correction, the longitudinal named entity recognition of diseases is studied. The aim is to summarize methods that can extract accurate information quickly and with less manual intervention.

[0025] Use HMM to solve named entity recognition annotations, that is, given a sequence of observations (1):

[0026] P(Y|X)=p(x 1 , n), X={x 1 , x 2 ,...x n} (1)

[0027] To find an optimal marker sequence (2) that maximizes the co...

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Abstract

The invention provides a named entity identification method for medical text data, and belongs to the technical field of information extraction. A hidden Markov model is used for carrying out sequence labeling on an original medical text to obtain a prediction word segmentation result. After prediction word segmentation processing is finished, a semi-supervised learning method is used for carrying out iterated self-learning on the word segmentation result to obtain an accurate and word segmentation and named entity identification result.

Description

technical field [0001] The invention relates to the technical field of information extraction, in particular to a named entity recognition method for medical text data. Background technique [0002] In the context of the current era of vigorously developing information technology, many medical institutions are building or have completed medical information systems. With the development and improvement of medical information systems, the accumulated medical data will provide reliable data support for the future research and development of medicine and information science. In recent years, the mathematical research on statistical data has been relatively mature, and the big data research on massive medical statistical data has also been carried out in full swing, which has played a good role in prediction and prevention and control. [0003] A large amount of text data, such as text medical records, medical literature, health information standards, etc., contains a lot of res...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06N99/00G06F19/00
CPCG06F40/279G06F40/284G06N20/00G16Z99/00
Inventor 史晟辉徐梓豪李五锁黄定琦陈晓宇张永健朱群雄林晓勇
Owner BEIJING UNIV OF CHEM TECH
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