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Big Data Analytics in Healthcare: Data-Driven Methods for Typical Treatment Pattern Mining

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Abstract

A huge volume of digitized clinical data is generated and accumulated rapidly since the widespread adoption of Electronic Medical Records (EMRs). These big data in healthcare hold the promise of propelling healthcare evolving from a proficiency-based art to a data-driven science, from a reactive mode to a proactive mode, from one-size-fits-all medicine to personalized medicine. This paper first discusses the research background - big data analytics in healthcare, the research framework of big data analytics in healthcare, analysis of medical process, and the literature summary of treatment pattern mining. Then the challenges for data-driven typical treatment pattern mining are highlighted, including similarity measure between treatment records, typical treatment pattern extraction, evaluation and recommendation, when considering the rich temporal and heterogeneous medical information in EMRs. Furthermore, three categories of typical treatment patterns are mined from doctor order content, duration, and sequence view respectively, which can provide a data-driven guideline to achieve the “5R” goal for rational drug use and clinical pathways.

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Acknowledgments

The authors would like to thank the anonymous referees for their help to improve the quality of the paper. This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 71771034 and 71421001, and Science and Technology Program of Jieyang under Grant No. 2017xm041.

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Authors and Affiliations

  1. Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, China

    Chonghui Guo & Jingfeng Chen

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  1. Chonghui Guo
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  2. Jingfeng Chen
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Correspondence to Chonghui Guo.

Additional information

The main contents of this paperwere originally presented as the invite keynote speech at the 19th International Symposium on Knolwdge and Systems Sciences in Tokyo during Nov 25–27, 2018.

Chonghui Guo is a professor of the Institute of Systems Engineering, Dalian University of Technology, Dalian, China. He received the B.S. degree in mathematics from Liaoning University in 1995, M.S. degree in operational research and control theory in 1999 and Ph.D. degree in management science and engineering from Dalian University of Technology in 2002. He was a postdoctoral research fellow in the Department of Computer Science in Tsinghua University, Beijing, China. His studies concentrate on data mining and knowledge discovery. He has published over 100 peer-reviewed papers in academic journals and conferences, besides 5 text-books and 2 monographs. He has been the Principal Investigator on over 10 research projects from the Government and the Industry.

Jingfeng Chen received the B.S. degree in mathematics from Henan University of Economics and Law in 2012, M.S. degree from Dongbei University of Finance and Economics in 2015, and Ph.D. degree from Institute of Systems Engineering, Dalian University of Technology, in 2019. His research interests include medical data mining, business intelligence. His papers have been published and presented on journals and conferences such as Journal of Biomedical Informatics, Health Policy and Technology, Soft Computing, Journal of Systems Science and Systems Engineering, and the 19th International Symposium on Knowledge and Systems Sciences (KSS2018).

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Guo, C., Chen, J. Big Data Analytics in Healthcare: Data-Driven Methods for Typical Treatment Pattern Mining. J. Syst. Sci. Syst. Eng. 28, 694–714 (2019). https://doi.org/10.1007/s11518-019-5437-5

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