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论文类型：期刊论文

发表时间：2019-12-01

发表刊物：JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING

收录刊物：EI、SCIE

卷号：28

期号：6

页面范围：694-714

ISSN号：1004-3756

关键字：Big data analytics; healthcare; electronic medical records; typical treatment pattern

摘要：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.