UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records

Alexander, N; Alexander, DC; Barkhof, F; Denaxas, S; (2020) Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records. Studies in Health Technology and Informatics , 270 pp. 499-503. 10.3233/SHTI200210. Green open access

[img]
Preview
Text
SHTI-270-SHTI200210.pdf - Published version

Download (468kB) | Preview

Abstract

Identifying subtypes of Alzheimer’s Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this study, we use UK primary care electronic health records (EHR) from the CALIBER resource to identify and characterize clinically-meaningful clusters patients using unsupervised learning approaches of MCA and K-means. We discovered and characterized five clusters with different profiles (mental health, non-typical AD, typical AD, CVD and men with cancer). The mental health cluster had faster rate of progression than all the other clusters making it a target for future research and intervention. Our results demonstrate that unsupervised learning approaches can be utilized on EHR to identify subtypes of heterogeneous conditions.

Type: Article
Title: Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/SHTI200210
Publisher version: https://doi.org/10.3233/SHTI200210
Language: English
Additional information: This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/
Keywords: Phenotyping, Alzheimer’s disease, Electronic health records, machine learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10103676
Downloads since deposit
55Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item