Temporal electronic phenotyping by mining careflows of breast cancer patients

• Published in: Journal of biomedical informatics Vol. 66; pp. 136 - 147
• Main Authors: Dagliati, A., Sacchi, L., Zambelli, A., Tibollo, V., Pavesi, L., Holmes, J.H., Bellazzi, R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-02-2017
• Subjects: Breast Neoplasms - diagnosis; Breast Neoplasms - therapy; Careflow mining; Data Mining; Delivery of Health Care; Electronic Health Records; Electronic phenotyping; Electronics; Female; Heterogeneous data sets; Humans; Patient Care - statistics & numerical data; Temporal data mining; Heterogeneous data sets; Careflow mining; Temporal data mining; Electronic phenotyping
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2016.12.012

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•A careflow mining approach designed to analyze heterogeneous longitudinal data is proposed.•Temporal electronic phenotyping of EHR and billing data is performed.•Better insights on the health care processes of breast cancer patients are obtained.
In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data.