{
  "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 developed using routine laboratory features, and their performance was evaluated using multiple metrics.Results The random forest (RF) model outperformed others using 49 routine lab features, achieving 82.71% accuracy in the internal cohort and 87.69% in external validation. SHapley Additive exPlanations (SHAP) model identified the top 10 critical features influencing model decisions, namely, chloride, sodium, gender, prealbumin, high-density lipoprotein, procalcitonin, albumin, globulin, total protein and creatine. Based on these indicators, an interactive web-based tool was developed ( https://mtb-ntm.streamlit.app).Discussion The features identified by the model align with established clinical parameters and existing studies. Certain previously underestimated variables, such as Cl and Na, exhibited substantial importance in distinguishing between MTB and NTM, offering valuable insights for the development of decision-support tools.Conclusion Routine laboratory indicators coupled with the RF model demonstrated potential capacity as an auxiliary diagnostic tool for discriminating MTB and NTM disease, offering effective medical support in resource-limited and remote settings.",
  "authors": [
    {
      "affiliations": [
        "The Marshall Centre for Infectious Diseases Research and Training, School of Biomedical Sciences, The University of Western Australia, Perth, Western Australia, Australia",
        "Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China"
      ],
      "name": "Jia-Wei Tang"
    },
    {
      "affiliations": [
        "Department of Laboratory Medicine, Yangzhou University affiliated Huai’an Hospital, Huai’an, Jiangsu Province, China",
        "Department of Laboratory Medicine, The Fifth People's Hospital of Huai'an, Huai’an, Jiangsu Province, China"
      ],
      "name": "Xue-Song Xiong"
    },
    {
      "affiliations": [
        "Department of Laboratory Medicine, Yangzhou University affiliated Huai’an Hospital, Huai’an, Jiangsu Province, China",
        "Department of Laboratory Medicine, The Fifth People's Hospital of Huai'an, Huai’an, Jiangsu Province, China"
      ],
      "name": "Ting-Ting Huang"
    },
    {
      "affiliations": [
        "Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China"
      ],
      "name": "Yu-Lu Zhang"
    },
    {
      "affiliations": [
        "Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China"
      ],
      "name": "Lin-Fei Yao"
    },
    {
      "affiliations": [
        "Department of Clinical Medicine, School of The First Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China"
      ],
      "name": "Wen-Wen Zhang"
    },
    {
      "affiliations": [
        "Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China",
        "School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, China"
      ],
      "name": "Yun-Yun Xie"
    },
    {
      "affiliations": [
        "Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China"
      ],
      "name": "Quan-Fa Liang"
    },
    {
      "affiliations": [
        "Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China"
      ],
      "name": "Zhi-Xuan Tan"
    },
    {
      "affiliations": [
        "Department of Pharmacy, The Fourth People’s Hospital of Huai’an, Huai’an, Jiangsu Province, China"
      ],
      "name": "Kun Jiang"
    },
    {
      "affiliations": [
        "Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China"
      ],
      "name": "Xin Liu"
    },
    {
      "affiliations": [
        "Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China",
        "School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, China",
        "Division of Microbiology and Immunology, School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia",
        "School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia",
        "School of Agriculture and Food Sustainability, University of Queensland, Brisbane, Queensland, Australia"
      ],
      "name": "Liang Wang"
    }
  ],
  "title": "Rapid discrimination of Mycobacterium tuberculosis and non-tuberculous mycobacteria disease via interpretive machine learning analysis of routine laboratory tests",
  "uid": "7bea7da2-7722-5287-a185-c2fd0ab31751"
}