上海交通大学学报（医学版）

上海交通大学学报(医学版) ›› 2020, Vol. 40 ›› Issue (07): 995-1000.doi: 10.3969/j.issn.1674-8115.2020.07.022

• 技术与方法 • 上一篇    下一篇

基于人工智能的病历后结构化专病数据库在临床研究中的价值探讨

荣雯雯1，汪 刚1，朱其立2   

  1. 1. 上海市胸科医院，上海交通大学附属胸科医院统计中心，上海 200030；2. 上海交通大学电子信息与电气工程学院，上海 200240
  • 出版日期:2020-07-28 发布日期:2020-09-23
  • 通讯作者: 汪 刚，电子信箱：chestwang@163.com。
  • 作者简介:荣雯雯（1991—），女，助理统计师，硕士；电子信箱：alicedely@163.com。
  • 基金资助:
    上海交通大学科技创新专项资金（ZH2018ZDA28）。

Discussion on value of medical records-structured specialized disease database based on artificial intelligence in clinical research

RONG Wen-wen1, WANG Gang1, ZHU Qi-li2   

  1. 1. Statistical Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China; 2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Online:2020-07-28 Published:2020-09-23
  • Supported by:
    Shanghai Jiao Tong University Scientific and Technological Innovation Funds (ZH2018ZDA28).

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摘要： 目的·探讨由非结构化电子病历文本信息建立的病历后结构化专病数据库在临床研究中的价值支撑。方法·采集2007年10月—2019年9月于上海市某三甲专科医院就诊的患者信息，采用人工智能（artificial intelligence，AI）引擎等信息化方法将电子病历文本信息后结构化形成结构化数据库，并与传统结构化数据库进行对比。结果·采集82 584例患者的就诊信息，住院文书记录结构化数量253 000条，搭建肺癌、食管癌、纵隔肿瘤3个专病数据库。与传统结构化数据库相比，该专病数据库扩大了数据检索范围，提升了数据检索效率。结论·基于AI的病历后结构化专病数据库的建成，减轻了临床医师数据检索的负担，为临床研究提供了有价值的统计数据。

关键词: 临床研究, 人工智能, 后结构化, 数据库

Abstract:

Objective · To explore the value support of medical records-structured specialized disease database established by using unstructured electronic medical record text information in clinical research. Methods · The information of patients who were admitted to a Grade A specialist hospital in Shanghai from Oct. 2007 to Sept. 2019 were collected. By using artificial intelligence (AI) engine and other information methods, the electronic medical record text information were structured into a structured database, and compared with the traditional structured database. Results · The information of 82 584 patients were collected, and the structured number of hospital records was 253 000. The specialized disease databases of lung cancer, esophageal cancer and mediastinal tumor were established. Compared with the traditional structured database, the specialized disease database expanded the scope of data retrieval and improved the efficiency of data retrieval. Conclusion · The construction of medical records-structured specialized disease database based on AI reduces the burden of clinician data retrieval, and provides valuable statistical data for clinical research.

Key words: clinical research, artificial intelligence (AI), structured, database

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