Temporal electronic phenotyping by mining careflows of breast cancer patients.

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.

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To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.