Tackling chronic diseases via computational phenotyping: Algorithms, tools and applications
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With the recent tsunami of medical data from electronic health records (EHRs), there has been a rise in interest in leveraging such data to improve efficiency of healthcare delivery and improve clinical outcomes. A large part of medical data science involves computational phenotyping, which leverages data driven methods to subtype and characterize patient conditions from heterogeneous EHR data. While many applications have used supervised phenotyping, unsupervised phenotyping will become increasingly more important in future precision medicine initiatives. A typical healthcare analytics workflow consists of phenotype discovery from EHR data, followed by predictive modeling that may leverage such phenotypes, followed by model deployment via avenues such as FHIR. To address unmet clinical needs, we have developed and demonstrated algorithms, tools and applications along each step of this process.