Loading...
dynaPhenoM: Dynamic Phenotype Modeling from Longitudinal Patient Records Using Machine Learning
Zhang, Hao Zang, Chengxi Xu, Jie Zhang, Hansi Fouladvand, Sajjad Havaldar, Shreyas Cheng, Feixiong Glicksberg, Benjamin S. Chen, Jin
Zhang, Hao
Zang, Chengxi
Xu, Jie
Zhang, Hansi
Fouladvand, Sajjad
Havaldar, Shreyas
Cheng, Feixiong
Glicksberg, Benjamin S.
Chen, Jin
Citations
Altmetric:
Genre
Pre-print
Date
2021-11-02
Advisor
Committee member
Group
Department
Health Service Administation and Policy
Subject
Permanent link to this record
Collections
Research Projects
Organizational Units
Journal Issue
DOI
https://doi.org/10.1101/2021.11.01.21265725
Abstract
Identification of clinically meaningful subphenotypes of disease progression can facilitate better understanding of disease heterogeneity and underlying pathophysiology. We propose a machine learning algorithm, termed dynaPhenoM, to achieve this goal based on longitudinal patient records such as electronic health records (EHR) or insurance claims. Specifically, dynaPhenoM first learns a set of coherent clinical topics from the events across different patient visits within the records along with the topic transition probability matrix, and then employs the time-aware latent class analysis (T-LCA) procedure to characterize each subphenotype as the evolution of these learned topics over time. The patients in the same subphenotype have similar such topic evolution patterns. We demonstrate the effectiveness and robustness of dynaPhenoM on the case of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) progression on three patient cohorts, and five informative subphenotypes were identified which suggest the different clinical trajectories for disease progression from MCI to AD.
Description
Citation
Citation to related work
medRxiv
Has part
ADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu