FuzzyGap: Sequential Pattern Mining for Predicting Chronic Heart Failure in Clinical Pathways

The rapid growth of electronic health records (EHRs) facilitates the use of clinical pathways, an actionable plan for patients which is represented as sequences of diagnostic records ordered by visit dates. We propose to extract discriminative and representative clinical pathways from EHRs using sequential pattern mining. However, existing sequential patterns cannot efficiently extract patterns due to patient variations in length and time period between visits. To resolve this problem, we propose FuzzyGap, a sequential pattern mining-based framework that extracts a discriminative subsequent pattern from the proper representation of the sequence of encounters which also emphasizes the last visit that is more significant than others. We demonstrate FuzzyGap using a case study of heart failure and show the effectiveness of sequential pattern mining