A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis.

ABSTRACT:

Background

Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results. In most settings, DST information is not available or is limited to isoniazid and fluoroquinolones. Whole genome sequencing could more accurately guide individualized treatment as the full drug resistance profile is obtained with a single test. Whole genome sequencing has not reached its full potential for patient care, in part due to the complexity of translating a resistance profile into the most effective individualized regimen.

Methods

We developed a treatment recommender clinic

SUBMITTER: Verboven L 

PROVIDER: S-EPMC11379382 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature