Rapid discrimination of <i>Mycobacterium tuberculosis</i> and non-tuberculous mycobacteria disease via interpretive machine learning analysis of routine laboratory tests.

ABSTRACT:

Objectives

Rapid discrimination of infections caused by Mycobacterium tuberculosis (MTB) and non-tuberculous mycobacteria (NTM) is crucial in clinical settings. Despite overlapping clinical and radiological features, the two require markedly different therapeutic approaches and public health responses. Current laboratory methods are time-consuming and complex, underscoring the urgent need for a simple and efficient diagnostic tool to inform public health decision-making.

Methods

Demographic, haematological and biochemical data were collected from two hospitals in Jiangsu province, China, between December 2018 and October 2024. A total of 400 patients were included in the training cohort, with 66 patients used for external validation. Six machine learning models were d

SUBMITTER: Tang JW 

PROVIDER: S-EPMC12542539 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature