Disease progression subtype discovery from longitudinal EMR data with a majority of missing values and unknown initial time points.
Year: 2014
Type: article
Abstract: Electronic medical records (EMR) contain a longitudinal collection of laboratory data that contains valuable phenotypic information on disease progression of a large collection of patients. These data can be potentially used in medical research or patient care; finding disease progression subtypes is a particularly important application. There are, however, two significant difficulties in utilizing this data for statistical analysis: (a) a large proportion of data is missing and (b) patients are in very different stages of disease progression and there are no well-defined start points of the time series. We present a Bayesian machine learning model that overcomes these difficulties. The method can use highly incomplete time-series measurement of varying lengths, it aligns together similar trajectories in different phases and is capable of finding consistent disease progression subtypes. We demonstrate the method on finding chronic kidney disease progression subtypes.
Source: PubMed Central
Authors: Ilkka Huopaniemi, Girish Nadkarni, Rajiv Nadukuru, Vaneet Lotay, Steve Ellis, Omri Gottesman, Erwin P Bottinger
Institution: Icahn School of Medicine at Mount Sinai
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: eMERGE Coordinating Center, EHR-based Genomic Discovery and Implementation, EMR Phenotypes and Community Engaged Genomic Associations, Integrative Analysis of a GWAS Repository with EMRs from over 40,000 Children, Mount Sinai Nephrology Research Training Grant, A Center for GEI Association Studies, Genome-wide Studies from the NUgene Biorespository, Genome-Wide Study of Cataract and Low HDL in the Personalized Medicine Research P, JH/CIDR Genotyping for Genome-Wide Association Studies, Development and Use of Network Infrastructure for High-Throughput GWA Studies, Genetic Discovery and Application in a Clinical Setting Continuing a Partnership, Biorepository for Genomic Medicine in diverse Communities, Geisinger eGenonic Medicine (GeM) Program, Better Outcomes for Children: GWAS & PheWAS in eMERGEII., Vanderbilt Genome-Electronic Records Project, Vanderbilt Genome Electronic Records Project, A Personalized Genomic Medicine Pilot Program Using the NJgene eMERGE Experience, IRIS: Incorporating Research Into Sight
OpenAlex ID: W2404247191