High-throughput Phenotyping with Temporal Sequences

Abstract

Electronic health records (EHRs) contain important temporal information about progression of disease and treatment outcomes. The objective of this paper is to propose and test a high-throughput approach for phenotyping using temporal sequences of EHR observations to derive predictive and interpretable data representations. Using expert labeled clinical data from a congestive heart failure (CHF) cohort, we applied a four-phase framework to mine new data representations from temporal sequences of EHR observations, reduce dimensionality of the feature space, evaluate feature importance, and classify CHF patients with CHF. We found that sequenced features significantly outperform raw EHR features in classifying heart failure patients. Compared to the gold standard created by a panel of domain experts, the best classifier using sequences of diagnosis and medication observations classified heart failure status with a receiver operating characteristic area under the curve of 0.926. Compared with their raw counterparts, the top sequenced features have lower prevalence in medical records, but present more useful information for classification as the majority of those who have the sequences are likely to have the disease. Raw EHR data are not direct reflections of true health states as they also reflect various healthcare processes. Our results demonstrated that data representations obtained from sequencing EHR observations can present novel insight about the progression of disease that is difficult to discern when clinical observation histories are treated independently. Harnessing knowledge of disease progression through temporal sequencing of EHR observations can improve computational disease classification or phenotyping.