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Discovering Patient Phenotypes Using Generalized Low Rank Models

Schuler, Alejandro and Liu, Vincent and Wan, Joe and Callahan, Alison and Udell, Madeleine and Stark, David E. and Shah, Nigam H. (2016) Discovering Patient Phenotypes Using Generalized Low Rank Models. In: 2016 Pacific Symposium on Biocomputing. World Scientific , Singapore, pp. 144-155. ISBN 978-981-4749-40-4. PMCID PMC4836913. http://resolver.caltech.edu/CaltechAUTHORS:20161116-144948434

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Abstract

The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a p henotype ), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, 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 low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1142/9789814749411_0014 DOIArticle
http://www.worldscientific.com/doi/abs/10.1142/9789814749411_0014PublisherArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4836913PubMed CentralArticle
Additional Information:© 2016 World Scientific. Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
PubMed Central ID:PMC4836913
Record Number:CaltechAUTHORS:20161116-144948434
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20161116-144948434
Official Citation:DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS ALEJANDRO SCHULER, VINCENT LIU, JOE WAN, ALISON CALLAHAN, MADELEINE UDELL, DAVID E. STARK, and NIGAM H. SHAH Biocomputing 2016. January 2016, 144-155
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72066
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Nov 2016 00:16
Last Modified:02 Jun 2017 16:07

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