DensityTransfer: A data driven approach for imputing electronic health records

Abstract

Patient Electronic Health Records (EHR) are systematic collection of electronic patient health information including demographics, diagnosis, medication, procedure, lab tests, etc. Because of the rapid development of hardware and storage technologies, more and more EHRs become available and they are now serving as the basis for a lot of medical informatics applications, such as predictive modeling, patient risk stratification and care pathway analysis. One major challenge or working with EHR is sparsity. This is because patients will only have EHR recorded when they paid visits to clinical facilities. However, the patients typically will not pay frequent visits to those clinical sites unless they are severely sick and need intensive monitoring. In this paper, we propose Density Transfer, a data driven approach for imputing the sparse patient EHRs. As its name suggests, the idea is to transfer knowledge from patients with denser EHRs to their similar patients with sparse EHRs. We formulate Density Transfer as an optimization problem and propose an efficient block coordinate descent based approach to solve it.