Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.

ABSTRACT:

Background

Coinfections of Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) impose a substantial global health burden. Patients with MTB infection face a heightened risk of progression to incident active TB, which preventive therapy can mitigate. Current testing methods often fail to identify individuals who subsequently develop incident active TB.

Methods

We developed random forest models to predict incident active TB using patients' medical data at HIV-1 diagnosis. Training our model involved using clinical data routinely collected at enrollment from the Swiss HIV Cohort Study (SHCS). This dataset encompassed 55 people with HIV (PWH) who developed incident active TB 6 months after enrollment and 1432 matched PWH without TB enrolled between 2000 and 2

SUBMITTER: Bartl L 

PROVIDER: S-EPMC12497954 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature