As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
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.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.