Patient-oriented unsupervised learning to uncover the patterns of multimorbidity associated with stroke using primary care electronic health records.

ABSTRACT:

Background

We aimed to identify and characterise the longitudinal patterns of multimorbidity associated with stroke.

Methods

We used an unsupervised patient-oriented clustering approach to analyse primary care electronic health records (EHR) of 30 common long-term conditions (LTC) in patients with stroke aged over 18, registered in 41 general practices in south London between 2005 and 2021.

Results

Of 849,968 registered patients, 9,847 (1.16%) had a record of stroke and 46.5% were female. The median age at record of stroke was 65.0 year (IQR: 51.5-77.0) and the median number of LTCs in addition to stroke was 3 (IQR: 2-5). We identified eight clusters of multimorbidity with contrasted socio-demographic characteristics (age, gender, and ethnicity) and risk factors. Besi

SUBMITTER: Delord M 

PROVIDER: S-EPMC11656854 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature