Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting.

ABSTRACT:

Background

Establishing risk factors associated with severity and prognosis in the early stages of the disease is important to identify patients who need specialized care. Creating new clinical tools to improve health decisions and outcomes in the population is essential.

Methods

This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC).We conducted a retrospective, longitudinal study using the SIDIAP database, part of the PHC Information System of Catalonia. The analysis included COVID-19 cases diagnosed in patients aged 18 and older from March 2020 to September 2022. Follow-up was conducted for 90 days post-diagnosis or until death. Various machine learning models of differing complexities were used

SUBMITTER: Barrot J 

PROVIDER: S-EPMC12522605 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature