POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study

TL;DR

POPDx is a deep learning framework that accurately imputes phenotype codes for UK Biobank participants, including rare and unobserved conditions, enhancing cohort definition for epidemiological research.

Contribution

This paper introduces POPDx, a novel bilinear machine learning method for multi-phenotype recognition that improves accuracy over existing approaches and handles incomplete data.

Findings

POPDx predicts rare and unobserved phenotypes effectively.

Significant improvement in multi-phenotype recognition across 22 disease categories.

Enables identification of epidemiological features associated with each phenotype.

Abstract

Objective For the UK Biobank standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants. Materials and Methods POPDx (Population-based Objective Phenotyping by Deep Extrapolation) is a bilinear machine learning framework for simultaneously estimating the probabilities of 1,538 phenotype codes. We extracted phenotypic and health-related information of 392,246 individuals from the UK Biobank for POPDx development and evaluation. A total of 12,803 ICD-10 diagnosis codes of the patients were converted to 1,538 Phecodes as gold standard labels. The POPDx framework was evaluated and compared to other available methods on automated multi-phenotype recognition. Results POPDx can predict phenotypes that are rare or even unobserved in training. We demonstrate substantial improvement of automated multi-phenotype recognition across 22 disease categories, and its application in identifying key epidemiological features associated with each phenotype. Conclusions POPDx helps provide well-defined cohorts for downstream studies. It is a general purpose method that can be applied to other biobanks with diverse but incomplete data.