arXiv：cs.LG· Joshua M. Jansen van V\"uren, Devendra S. Parihar, Daphne Naidoo, Marisa Klopper, Frank Cobelens, Lutz Kolbe, Kimsey Zajac, Willy Ssengooba, Moses Joloba, Grant Theron, Thomas R. Niesler·· 3 小时前AI 评分27
基于乌干达与南非临床数据的跨队列结核病分类研究
Cross-cohort TB classification using clinical data gathered in Uganda and South Africa
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研究首次评估机器学习用于南非和乌干达两国采集的患者临床与人口数据，以筛查可从昂贵分子检测中获益的结核病患者。基于 CAGE-TB 数据集，逻辑回归（LR）、多层感知机（MLP）和卷积神经网络（CNN）结合贪心特征选择，特征选择使开发集 AUROC 提升 2-7%。LR 在留出的乌干达和南非数据上 AUROC 分别达 0.8 和 0.84，灵敏度较 WHO 最低要求低 4-9%。
正文
Authors:Joshua M. Jansen van Vüren, Devendra S. Parihar, Daphne Naidoo, Marisa Klopper, Frank Cobelens, Lutz Kolbe, Kimsey Zajac, Willy Ssengooba, Moses Joloba, Grant Theron, Thomas R. Niesler
Abstract:We present a first evaluation of machine learning applied to patient clinical and demographic data gathered in two different countries for the purpose of tuberculosis (TB) screening to identify people who would benefit from expensive molecular testing. Experiments are based on the recently-compiled CAGE-TB dataset, which includes sub-cohorts of people with presumptive TB presenting at community health care centres in South Africa and Uganda. Three neural network architectures (logistic regression (LR), multilayer perceptrons (MLP) and convolutional neural networks (CNN)) are considered in conjunction with greedy feature selection. For the convolutional neural network, a strategy that jointly optimises feature selection and feature ordering is proposed and shown to lead to consistent development and test set improvements. For all three models, development set area under the receiver operating characteristic (AUROC) curve is improved by 2-7% using feature selection. LR after feature selection achieves an AUROC of 0.8 [0.75,0.86] (95% CI) and 0.84 [0.78,0.9] when testing on the held-out Ugandan and South African data respectively. Although outperforming LR on the development cohort, the deeper networks (MLP, CNN) show inconsistent trends on the held-out cohorts, while LR achieves performance within 1-2% of the best achieved in terms of AUROC. LR narrowly misses the WHO minimum requirements by 4-9% in sensitivity even though the network is being evaluated on a completely held-out cohort. The development of neural-network based classifiers for TB screening therefore appears viable.
| Comments: | Accepted: SATNAC, Drakensberg, South Africa, 2026 |
| Subjects: | Machine Learning (cs.LG) |
| MSC classes: | 68T07 (Primary) 62H30, 62P10, 92C50, 62J12 (Secondary) |
| ACM classes: | I.2.6; I.5.1; I.5.2; J.3 |
| Cite as: | arXiv:2610.03256 [cs.LG] |
| (or arXiv:2610.03256v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03256 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Joshua Miles Jansen Van Vüren [view email]
[v1]
Fri, 2 Oct 2026 13:01:26 UTC (255 KB)
来源：arXiv：cs.LG · arxiv.org