The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients#R##N#to inform corresponding treatment. Given a patient grouping (hereafter referred to as a p henotype ), clinicians can#R##N#implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally,#R##N#phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these#R##N#approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our#R##N#understanding of disease has progressed substantially in the past century, there are still important domains in which#R##N#our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery,#R##N#researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by#R##N#missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low#R##N#Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we#R##N#analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains#R##N#upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second,#R##N#we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that#R##N#low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.
['Alejandro Schuler', 'Vincent Liu', 'Joe Wan', 'Alison Callahan', 'Madeleine Udell', 'David E. Stark', 'Nigam H. Shah']
DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS.