EAGER: Patient Similarity Learning with Massive Clinical Data and Its Applications in Cohort Identification

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The rapid adoption of Electronic Health Records (EHRs) across the U.S. healthcare systems coupled with the capability of linking EHRs to research biorepositories provides a unique opportunity for conducting large-scale Precision Medicine research. A critical step to make such research possible is identification of cohorts by defining inclusion and exclusion criteria that algorithmically select sets of patients based on available clinical data. For most of the existing research, the criteria for generating those patient cohorts are defined manually, which makes the entire process slow, labor intensive and not scalable. This project develops patient similarity learning algorithms to enable automatic cohort identification, which will accelerate the research of precision medicine.The massive clinical data around patients are highly heterogeneous and sparse. Although there are some patient similarity learning algorithms, they typically work with a single type of patient data (e.g., just using diagnosis information in patient EHR) and cannot handle those challenges mentioned above effectively. This project develops advanced patient similarity learning algorithms by 1) learning composite patient similarities through a refinement process from multiple base similarity measures, with each base similarity being evaluated from a specific source of patient data or a specific form of patient representation; and 2) integrating information from multiple related auxiliary domains, such as drug, disease, and genomic information. Those information effectively regularizes the patient similarity learning process and makes it less sensitive to data sparsity.