Phenotype Detection in Real World Data via Online MixEHR Algorithm
This work addresses the need for efficient clinical development and disease risk understanding by scaling up phenotype detection for healthcare applications, though it is incremental as it builds on an existing method.
The authors tackled the problem of detecting disease phenotypes from large-scale electronic health records and claims data by extending an existing unsupervised algorithm to an online version, enabling analysis of datasets an order of magnitude larger and discovering clinically meaningful disease subtypes and comorbidities.
Understanding patterns of diagnoses, medications, procedures, and laboratory tests from electronic health records (EHRs) and health insurer claims is important for understanding disease risk and for efficient clinical development, which often require rules-based curation in collaboration with clinicians. We extended an unsupervised phenotyping algorithm, mixEHR, to an online version allowing us to use it on order of magnitude larger datasets including a large, US-based claims dataset and a rich regional EHR dataset. In addition to recapitulating previously observed disease groups, we discovered clinically meaningful disease subtypes and comorbidities. This work scaled up an effective unsupervised learning method, reinforced existing clinical knowledge, and is a promising approach for efficient collaboration with clinicians.