On mining clinical pathway patterns from medical behaviors

  • Authors:

  • Zhengxing Huang;Xudong Lu;Huilong Duan

  • Affiliations:

  • College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqin building 510, Zheda road 38#, Hangzhou, 310008 Zhejiang, China;College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqin building 510, Zheda road 38#, Hangzhou, 310008 Zhejiang, China;College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqin building 510, Zheda road 38#, Hangzhou, 310008 Zhejiang, China

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2012

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Abstract

Objective: Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Clinical pathway pattern mining is one of the most important components of clinical pathway analysis and aims to discover which medical behaviors are essential/critical for clinical pathways, and also where temporal orders of these medical behaviors are quantified with numerical bounds. Even though existing clinical pathway pattern mining techniques can tell us which medical behaviors are frequently performed and in which order, they seldom precisely provide quantified temporal order information of critical medical behaviors in clinical pathways. Methods: This study adopts process mining to analyze clinical pathways. The key contribution of the paper is to develop a new process mining approach to find a set of clinical pathway patterns given a specific clinical workflow log and minimum support threshold. The proposed approach not only discovers which critical medical behaviors are performed and in which order, but also provides comprehensive knowledge about quantified temporal orders of medical behaviors in clinical pathways. Results: The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to six specific diseases, i.e., bronchial lung cancer, gastric cancer, cerebral hemorrhage, breast cancer, infarction, and colon cancer, in two years (2007.08-2009.09). As compared to the general sequence pattern mining algorithm, the proposed approach consumes less processing time, generates quite a smaller number of clinical pathway patterns, and has a linear scalability in terms of execution time against the increasing size of data sets. Conclusion: The experimental results indicate the applicability of the proposed approach, based on which it is possible to discover clinical pathway patterns that can cover most frequent medical behaviors that are most regularly encountered in clinical practice. Therefore, it holds significant promise in research efforts related to the analysis of clinical pathways.