Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.

ABSTRACT:

Purpose

The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data.

Participants

All individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related ou

SUBMITTER: Manemann SM 

PROVIDER: S-EPMC8190051 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature