Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records
Daniel C Alexander
Studies in health technology and informatics
Identifying and evaluating clinical subtypes of Alzheimers disease in care electronic health records using unsupervised machine learning
P1-554 DISCOVERING SUBTYPES OF ALZHEIMER�S DISEASE USING ELECTRONIC HEALTH RECORDS
Impact on life expectancy of temporal sequencing in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure using linked data for 1.7 million individuals in Wales with 20-year follow-up
ClustEHR: a tool for generating synthetic EHR data for unsupervised learning experiments.
Trajectories of Disease Accumulation Using Electronic Health Records