Process and methods of clinical big data mining based on electronic medical records

Tong RUAN; Ju GAO; Donglei FENG; Xiyuan QIAN; Ting WANG; Chenglin SUN

doi:10.11959/j.issn.2096-0271.2017054

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Process and methods of clinical big data mining based on electronic medical records

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- Process and methods of clinical big data mining based on electronic medical records
- Big data research Vol. 3, Issue 5, Pages: 2017054(2017)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2017054
  CLC： TP311.13
- Online First：2017-09，
  
  Published：20 September 2017
- 稿件说明：
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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：

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

王茜 . 英国大数据战略分析 [J ] . 全球科技经济瞭望 , 2013 ( 8 ): 24 - 27 .

WANG X . British state strategy of developing big data [J ] . Global Science,Technology and Economy Outlook , 2013 ( 8 ): 24 - 27 .

王忠 . 美国推动大数据技术发展的战略价值及启示 [J ] . 中国发展观察 , 2012 ( 6 ): 44 - 45 .

WANG Z . The strategic value and enlightenment of promoting big data technology development in America [J ] . China Development Observation , 2012 ( 6 ): 44 - 45 .

BROWN J S , HOLMES J H , SHAH K , et al . Distributed health data networks:a practical and preferred approach to multiinstitutional evaluations of comparative effectiveness,safety,and quality of care [J ] . Med Care , 2010 , 48 ( 6 ): 45 - 51 .

SHERMAN R E , ANDERSON S A , DALPAN G J , et al . Real-world evidencewhat is it and what can it tell us [J ] . New England Journal of Medicine , 2016 , 375 ( 23 ):2293.

董景五 . 疾病和有关健康问题的国际统计分类第十次修订本（ICD-10） [M ] . 北京 : 人民卫生出版社 , 1996 .

DONG J W . The international statistical classification of diseases and related health problems 10th revision [M ] . Beijing : People’s Medical Publishing HousePress , 1996 .

BODENREIDER O . The unified medical language system (UMLS):integrating biomedical terminology [J ] . Nucleic Acids Research , 2004 , 32 (suppl 1): D267 - D270 .

WEINSTEIN J N , COLLISSON E A , MILLS G B , et al . The cancer genome atlas pan-cancer analysis project [J ] . Nature Genetics , 2013 , 45 ( 10 ): 1113 - 1120 .

SAMWALD M , JENTZSCH A , BOUTON C , et al . Linked open drug data for pharmaceutical research and development [J ] . Journal of Cheminformatics , 2011 , 3 ( 1 ):19.

BELLEAU F , NOLIN M A , TOURIGNY N , et al . Bio2RDF:towards a mashup to build bioinformatics knowledge systems [J ] . Journal of Biomedical Informatics , 2008 , 41 ( 5 ): 706 - 716 .

HEARST M A , . Automatic acquisition of hyponyms from large text corpora [C ] // The 14th Conference on Computational Linguistics,August 23-28,1992,Nantes,France . New York:ACM Press , 1992 : 539 - 545 .

DALKEY N C , ROURKE D L . Experimental assessment of Delphi procedures with group value judgements:advanced research projects agency [J ] . Cluster Analysis , 1971 :58.

CHENG Y , WANG F , ZHANG P , et al . Risk prediction with electronic health records:a deep learning approach [C ] // The 2016 SIAM International Conference on Data Mining,May 5-7,2016,Miami,USA.[S.l.:s.n.] , 2016 : 432 - 440 .

SUTHERLAND S M , CHAWLA L S,KANE-GILL S L , et al . Utilizing electronic health records to predict acute kidney injury risk and outcomes:workgroup statements from the 15th,ADQI consensus conference [J ] . Canadian Journal of Kidney Health ＆ Disease , 2016 , 3 ( 1 ): 1 - 14 .

WOLFSON J , BANDYOPADHYAY S , ELIDRISI M , et al . A naive Bayes machine learning approach to risk prediction using censored,time-to-event data [J ] . Statistics in Medicine , 2014 , 34 ( 21 ): 2941 - 2957 .

马宗帅 . 基于深度学习的心脑血管疾病预测方法研究 [D ] . 西安:西安建筑科技大学 , 2015 .

MA Z S . Research on cardiovascular disease prediction based on deep learning technical [D ] . Xi’an:Xi’an University of Architecture and Technology , 2015 .

AULI M , GALLEY M , QUIRK C , et al . Joint language and translation modeling with recurrent neural networks [J ] . American Journal of Psychoanalysis , 2013 , 74 ( 2 ): 212 - 213 .

RUFFINI G , IBAÑEZ D , CASTELLANO M , et al . EEG-driven RNN classification for prognosis of neurodegeneration in at-risk patients [C ] // International Conference on Artificial Neural Networks,September 6-9,2016,Barcelona,Spain . Berlin:Springer , 2016 : 306 - 313 .

MIOTTO R , LI L , DUDLEY J T . Deep learning to predict patient future diseases from the electronic health records [M ] . Berlin : Springer International PublishingPress , 2016 .

LIU L , TANG J , CHENG Y , et al . Mining diabetes complication and treatment patterns for clinical decision support [C ] // The 22nd ACM international conference on Information ＆ Knowledge Management,October 27-November 1,2013,San Francisco,USA . New York:ACM Press , 2013 : 279 - 288 .

HUANG Z , DONG W , BATH P , et al . On mining latent treatment patterns from electronic medical records [J ] . Data Mining＆ Knowledge Discovery , 2015 , 29 ( 4 ): 1 - 36 .

SUTHERLAND S M , CHAWLA L S , KANEGILL S L , et al . Utilizing electronichealth records to predict acute kidney injury risk and outcomes:workgroup statements from the 15th ADQI consensus conference [J ] . Canadian Journal of Kidney Health and Disease , 2016 , 3 ( 1 ): 1 - 14 .

WOLFSON J , BANDYOPADHYAY S , ELIDRISI M , et al . A naive Bayes machine learning approach to risk prediction using censored,time-to-event data [J ] . Statistics in Medicine , 2011 , 34 ( 21 ): 2941 - 2957 .

SUTSKEVER I , VINYALS O , LE Q V . Sequence to sequence learning with neural networks [C ] // The 27th International Conference on Neural Information Processing Systems,December 8-13,2014,Montreal,Canada . New York:ACM Press , 2014 : 3104 - 3112 .

ZHANG J , MANI I . kNN approach to unbalanced data distributions:a case study involving information extraction [C ] // The ICML 2003 Workshop on Learning from Imbalanced Datasets,December 3-8,2003,Piscataway,USA.[S.l.:s.n.] , 2003 .

TOMEK I . Two modifications of CNN [J ] . IEEE Transactions on Systems Man and Communications , 1976 ,SMC-6( 11 ): 769 - 772 .

KUBAT M , MATWIN S . Addressing the course of imbalanced training sets:onesided selection [C ] // The 14th International Conference on Machine Learning (ICML 1997),July 8-12,1997,Nashville,USA.[S.l.:s.n.], . 1997 : 179 - 186 .

WILSOND L . Asymptotic properties of nearest neighbor rules using edited data [J ] . IEEE Transactions on Systems,Man,and Communications , 2007 ,SMC-2( 3 ): 408 - 421 .

LAURIKKALA J , . Improving identification of difficult small classes by balancing class distribution [C ] // Conference on Artificial Intelligence in Medicine in Europe,July 1-4,2001,Cascais,Portugal . Berlin:Springer Berlin Heidelberg , 2001 : 63 - 66 .

CHAWLAN V , BOWYER K W , HALL L O , et al . SMOTE:synthetic minority oversampling technique [J ] . Journal of Artificial Intelligence Research , 2002 ( 16 ): 321 - 357 .

ROSENBAUM P R , RUBIN D B . The central role of the propensity score in observational studies for causal effects [J ] . Biometrika , 1983 , 70 ( 1 ): 41 - 55 .

SOBEL M E , . Causal inference in the social and behavioral sciences [M ] // Handbook of Statistical Modeling for the Social and Behavioral Sciences . New York:Springer US , 1995 : 1 - 38 .

HOLLAND P W . Statistics and causal inference [J ] . Journal of the American Statistical Association , 1986 , 81 ( 396 ): 945 - 960 .

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