PT  - JOURNAL ARTICLE
AU  - Brett K. Beaulieu-Jones
AU  - Casey S. Greene
AU  - the Pooled Resource Open-Access ALS Clinical Trials Consortium
TI  - Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification
AID  - 10.1101/039800
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 039800
4099  - http://biorxiv.org/content/early/2016/07/26/039800.short
4100  - http://biorxiv.org/content/early/2016/07/26/039800.full
AB  - Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to extract electronic phenotypes, which can be paired with genetic data to identify genes underlying common human diseases. This task remains challenging: high quality phenotyping is costly and requires physician review; many fields in the records are sparsely filled; and our definitions of diseases are continuing to improve over time. Here we develop and evaluate a semi-supervised learning method for EHR phenotype extraction using denoising autoencoders for phenotype stratification. By combining denoising autoencoders with random forests we find classification improvements across multiple simulation models and improved survival prediction in ALS clinical trial data. This is particularly evident in cases where only a small number of patients have high quality phenotypes, a common scenario in EHR-based research. Denoising autoencoders perform dimensionality reduction enabling visualization and clustering for the discovery of new subtypes of disease. This method represents a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs.HIGHLIGHTSDenoising autoencoders (DAs) can model electronic health records.Semi-supervised learning with DAs improves ALS patient survival predictions.DAs improve patient cluster visualization through dimensionality reduction.