AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Journal: Journal of biomedical informatics
Published Date: Nov 23, 2021

Abstract

BACKGROUND: Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on clinician's knowledge, suggesting an unmet need for a robust and efficient generic score-generating method.

Authors

  • Feng Xie
    School of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: 1111705006@mail2.gdut.edu.cn.
  • Yilin Ning
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
  • Han Yuan
    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Benjamin Alan Goldstein
    Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Marcus Eng Hock Ong
    Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore. marcus.ong.e.h@sgh.com.sg.
  • Nan Liu
    Duke-NUS Medical School Centre for Quantitative Medicine Singapore Singapore.
  • Bibhas Chakraborty
    Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.

Keywords

External Resources

Popular Topics
Recent Journals