Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.

Journal: BMC medical research methodology
Published Date:

Abstract

BACKGROUND: Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.

Authors

  • Yinan Huang
    Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, 4849 Calhoun Road, Health & Sciences Bldg 2, Houston, TX, 77204, USA.
  • Jieni Li
    Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, 77204, USA.
  • Mai Li
  • Rajender R Aparasu
    Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, 4849 Calhoun Road, Health & Sciences Bldg 2, Houston, TX, 77204, USA. rraparasu@uh.edu.