基于NLP的中医医案文本快速结构化方法

doi:10.11959/j.issn.2096-0271.2022025

Abstract

Abstract:

Traditional Chinese medicine (TCM) medical records are the most valuable documents for TCM doctors to learn clinical experience.The structured TCM medical records are conducive to extract the clinic knowledge based on machine learning and other methods, which can accelerate the inheritance of TCM.A fast text structuring methodology of TCM medical records based on natural language processing(NLP)was proposed to structure the clinic cases.Essence of Chinese Modern Famous Chinese Medical Records was selected as the medical record structuring objects,and the text in the screenshots of the medical records was recognized by optical character recognition (OCR) and the text was initially structured.A simple symptom dictionary was constructed, and the improved N-gram model combined with the dictionary was used to recognize the symptoms, signs and other words in the text, and the dictionary was updated in the structuring process.At last, 4 754 text medical records were structured.The final model was test on 666 medical records selected randomly from the corpus, and its F1 value reached 82.99%.

Key words: N-gram model, NLP, TCM medical records, Chinese word segmentation, OCR

CLC Number:

TP391.1

Xiaoxia XIAO, Mingting LIU, Fengtianci YANG, Jianjianxian LIU, Yang YANG, Yue SHI. A fast text structuring methodology of TCM medical records based on NLP[J]. Big Data Research, 2022, 8(3): 128-139.

Figures/Tables 11

References 14

[1]	滕文静, 孙长岗, 李雁 . 浅谈不同中医医案研究方法对临床思维建立的重要性[J]. 中华中医药杂志, 2018,33(3): 811-815.
	TENG W J , SUN C G , LI Y . Discussion on the importance about different research methods of traditional Chinese medicine cases to the establishment of clinical thinking[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2018,33(3): 811-815.
[2]	肖晓霞 . 基于机器学习的中医临床症状数据元研究[D]. 长沙:湖南中医药大学, 2018.
	XIAO X X . Research on clinical symptom data elements of traditional Chinese medicine based on machine learning[D]. Changsha:Hunan University of Chinese Medicine, 2018.
[3]	翟凤文, 赫枫龄, 左万利 . 字典与统计相结合的中文分词方法[J]. 小型微型计算机系统, 2006,27(9): 1766-1771.
	ZHAI F W , HE F L , ZUO W L . Chinese word segmentation based on dictionary and statistics[J]. Journal of Chinese Computer Systems, 2006,27(9): 1766-1771.
[4]	蒋建洪, 赵嵩正, 罗玫 . 词典与统计方法结合的中文分词模型研究及应用[J]. 计算机工程与设计, 2012,33(1): 387-391.
	JIANG J H , ZHAO S Z , LUO M . Analysis and application of Chinese word segmentation model which consist of dictionary and statistics method[J]. Computer Engineering and Design, 2012,33(1): 387-391.
[5]	唐琳, 郭崇慧, 陈静锋 . 中文分词技术研究综述[J]. 数据分析与知识发现, 2020,4(S1): 1-17.
	TANG L , GUO C H , CHEN J F . Review of Chinese word segmentation studies[J]. Data Analysis and Knowledge Discovery, 2020,4(S1): 1-17.
[6]	何晗 . 自然语言处理入门[M]. 北京: 人民邮电出版社, 2019.
	HE H . Introduction natural language processing[M]. Beijing: Posts ＆Telecom Press, 2019.
[7]	张帆, 刘晓峰, 孙燕 . 中医医案文献自动分词研究[J]. 中国中医药信息杂志, 2015,22(2): 38-41.
	ZHANG F , LIU X F , SUN Y . Study on automatic word segmentation for traditional Chinese medical record literature[J]. Chinese Journal of Information on Traditional Chinese Medicine, 2015,22(2): 38-41.
[8]	李明浩, 刘忠, 姚远哲 . 基于LSTM-CRF的中医医案症状术语识别[J]. 计算机应用, 2018,38(S2): 42-46.
	LI M H , LIU Z , YAO Y Z . LSTM-CRF based symptom term recognition on traditional Chinese medical case[J]. Journal of Computer Applications, 2018,38(S2): 42-46.
[9]	王永炎, 陶广正 . 中国现代名中医医案精粹第5集[M]. 北京: 人民卫生出版社, 2010.
	WANG Y Y , TAO G Z . The essence of medical record from famous modern TCM doctor-episode 5[M]. Beijing: People’s Medical Publishing House, 2010.
[10]	MARTIN J H . Speech and language processing:an introduction to natural language processing,computational linguistics,and speech recognition[M]. Upper Saddle River: Pearson/Prentice Hall, 2009: 30-31.
[11]	吴旭东 . 正向最大匹配分词算法的分析与改进[J]. 科技传播, 2011,3(20): 164-165.
	WU X D . Analysis and improvement of forward maximum matching word segmentation algorithm[J]. Public Communication of Science ＆ Technology, 2011,3(20): 164-165.
[12]	李灿东 . 中医诊断学(新世纪第4版)[M]. 北京: 中国中医药出版社, 2016.
	LI C D . Diagnostics of traditional Chinese medicine (new century 4th edition)[M]. Beijing: China Press of Traditional chinese Medicine, 2016.
[13]	成战鹰, 王肖龙 . 诊断学基础(第2版)[M]. 北京: 人民卫生出版社, 2016.
	CHENG Z Y , WANG X L . Fundamentals of diagnostics (2nd edition)[M]. Beijing: People’s Medical Publishing House, 2016.
[14]	BIRD S , KLEIN E , LOPER E . Natural language processing with Python[M]. California: O’Reilly Media Inc., 2009.

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分割关键词	同义词或对应结构词
女	女孩、女士、妇、妇女、小姐
初诊	主诉及病史、诊查
治法	治则、操作
处方	方药

词库	内容
前缀修饰词库	无、不、多、右、左、未、有、稍、微、频、两、较、二、少、易、出、欲……

词库	内容
西医特殊处理的关键词库	次/分、CT、血检、血象、妇检、尿检、血常规、B超检查、胃镜、病理检查、肝功能检查、胃黏膜活检、大便检查、尿常规……

书籍序号	文本字数/千字
1	1 165
2	1 126
3	1 203
4	1 382
5	1 050
6	947
总计	6 873

方案	正确症状总数/个	分词总数/个	有效医案总数/份
手工标准数据	5 439	5 439	666
词典	4 280	5 273	664
N-gram模型	1 878	3 400	638
词典+N-gram模型	4 669	5 812	664

A fast text structuring methodology of TCM medical records based on NLP

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Figures/Tables 11

References 14

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方案	P	R	F1值
词典	81.17%	78.69%	79.91%
N-gram	55.24%	34.53%	42.50%
词典+N-gram	80.33%	85.84%	82.99%

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