Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England.

ABSTRACT:

Background

Identifying clusters of diseases may aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Our study aims to identify disease clusters using a large set of long-term conditions and comparing methods that use the co-occurrence of diseases versus methods that use the sequence of disease development in a person over time.

Methods

We use electronic health records from over ten million people with multimorbidity registered to primary care in England. First, we extract data-driven representations of 212 diseases from patient records employing (i) co-occurrence-based methods and (ii) sequence-based natural language processing methods. Second, we apply the graph-based Markov Multiscale Community Detection (MMCD

SUBMITTER: Beaney T 

PROVIDER: S-EPMC11137021 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature