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Supporting Information to "Development of an open-access and explainable machine learning prediction system to assess the mortality and recurrence risk factors of Clostridioides difficile infection patients: Model Training and Hyperparameter Optimization with Cross-Validation"
  • +9
  • Yui-Lun Ng,
  • Chi-Kiu Lo,
  • Kit-Hang Lee,
  • Xiaochen Xie,
  • Thomas N.Y. Kwong,
  • Margaret Ip,
  • Lin Zhang,
  • Jun Yu,
  • Joseph J.Y. Sung,
  • William K.K. Wu,
  • Sunny H. Wong,
  • Ka-Wai Kwok
Yui-Lun Ng
Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong
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Chi-Kiu Lo
Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong
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Kit-Hang Lee
Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong
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Xiaochen Xie
Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong
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Thomas N.Y. Kwong
SKL Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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Margaret Ip
Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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Lin Zhang
SKL Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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Jun Yu
SKL Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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Joseph J.Y. Sung
SKL Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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William K.K. Wu
Department of Anaesthesia and Intensive Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, SKL Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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Sunny H. Wong
SKL Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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Ka-Wai Kwok
Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong
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Abstract

Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence will facilitate prevention, timely treatment and improve clinical outcomes. We aim to establish an open-access web-based prediction system, which estimates CDI patients’ mortality and recurrence outcomes, and explains the machine learning prediction with patients’ characteristics. Prognostic models were developed using four various types of machine learning algorithms and statistical logistics regression model utilizing over 15,000 CDI patients from 41 hospitals in Hong Kong. The boosting-based machine learning algorithm Gradient Boosting Machine (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperformed statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. The open-access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/. In this article, we explain the development of machine learning models and illustrate how to apply hyperparameter tuning with cross-validation to optimize the model accuracy.

Peer review timeline

19 Oct 2020Submitted to AISY Supporting Information
21 Oct 2020Published in AISY Supporting Information