基于电子病历的临床医疗大数据挖掘流程与方法

阮彤; 高炬; 冯东雷; 钱夕元; 王婷; 孙程琳

doi:10.11959/j.issn.2096-0271.2017054

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基于电子病历的临床医疗大数据挖掘流程与方法

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- 基于电子病历的临床医疗大数据挖掘流程与方法
- Process and methods of clinical big data mining based on electronic medical records
- 大数据 2017年3卷第5期页码：2017054
- 作者机构：
  
  1. 华东理工大学，上海 200237
  2. 上海曙光医院，上海 200025
  3. 万达信息股份有限公司，上海 200233
- 作者简介：
  
  [ "阮彤（1973-），女，博士，华东理工大学计算机技术研究所教授、所长，自然语言处理与大数据挖掘实验室主任，主要研究方向为文本抽取、知识图谱、数据质量评估等。" ]
  [ "高炬（1966-），男，上海曙光医院副院长、主任医师，主要研究方向为医院行政管理及中西医结合肝胆病研究。" ]
  [ "冯东雷（1972-），男，博士，万达信息股份有限公司教授级高级工程师，主要研究方向为健康医疗大数据+人工智能、健康医疗+互联网、区域人口健康信息化、卫生信息标准化等。" ]
  [ "钱夕元（1968-），男，博士，华东理工大学教授，主要研究方向为统计计算、数值软件等。" ]
  [ "王婷（1993-），女，华东理工大学硕士生，主要研究方向为知识图谱、信息抽取。" ]
  [ "孙程琳（1993-）女，华东理工大学硕士生，主要研究方向为知识图谱、问答系统。" ]
- 基金信息：
  
  国家高技术研究发展计划（“863”计划）基金资助项目;The National High Technology Research and Development Program of China (863 Program)(2015AA020107);国家科技支撑基金资助项目;National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2015BAH12F01-05)
- DOI：10.11959/j.issn.2096-0271.2017054
  中图分类号： TP311.13
- 网络首发：2017-09，
  
  纸质出版：2017-09-20
- 稿件说明：
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阮彤, 高炬, 冯东雷, 等. 基于电子病历的临床医疗大数据挖掘流程与方法[J]. 大数据, 2017,3(5):2017054.

Tong RUAN, Ju GAO, Donglei FENG, et al. Process and methods of clinical big data mining based on electronic medical records[J]. Big data research, 2017, 3(5): 2017054.
阮彤, 高炬, 冯东雷, 等. 基于电子病历的临床医疗大数据挖掘流程与方法[J]. 大数据, 2017,3(5):2017054. DOI： 10.11959/j.issn.2096-0271.2017054.

Tong RUAN, Ju GAO, Donglei FENG, et al. Process and methods of clinical big data mining based on electronic medical records[J]. Big data research, 2017, 3(5): 2017054. DOI： 10.11959/j.issn.2096-0271.2017054.

摘要

以医院电子病历为核心的临床数据记录了病人的疾病、诊断和治疗信息。挖掘此类数据，可以辅助医生进行临床科研与临床诊疗。首先提出了临床大数据挖掘过程中碰到的各项难题，总结了临床医疗大数据挖掘的核心流程，流程包括以临床数据集成、基于知识图谱的临床专病库的构建过程、电子病历数据质量的评估方法以及以临床疗效分析与疾病预测为核心的临床医疗大数据应用等任务，进而对流程中的每个任务提出了解决方案，给出了实验结果。最后，展望了未来临床电子病历挖掘应用和技术的发展。

Abstract

Electronic medical records from hospitals record the patient's disease

diagnosis and treatment information.It forms the basis of clinical data.Mining such data can assist doctors in clinical research and clinical diagnosis and treatment.Firstly

challenges encountered in the process of big data mining on EMR were raised

then the core process was summarized.The process includes tasks such as clinical data integration

the construction of clinical specialist disease database based on knowledge graph

the quality assessment methods on EMR

and comparative effectiveness and risk prediction of diseases as the core of clinical big data applications.A solution for each task was proposed

and the experimental results were given.Finally

the future directions of technologies and applications of big data mining on healthcare were presented.

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references

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