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

• Published in: BMC medical research methodology Vol. 23; no. 1; pp. 268 - 11
• Main Authors: Huang, Yinan, Li, Jieni, Li, Mai, Aparasu, Rajender R.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.11.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Artificial intelligence; Cancer; Care and treatment; Chronic diseases; Datasets; Diagnosis; Disease; Electronic health records; Event history analysis; Health aspects; Health Sciences; Health services; Humans; Machine Learning; Medical prognosis; Medical research; Medicine; Medicine & Public Health; Methods; Neural network; Neural networks; Neural Networks, Computer; Oncology; Patient outcomes; Patients; Prediction Methods for Rare Diseases or Outcomes; Prognosis; Random survival forest; Real-world datasets; Statistical Theory and Methods; Statistics for Life Sciences; Survival analysis; Theory of Medicine/Bioethics; Treatment Outcome; United States; United States > US; Real-world datasets; Random survival forest; Neural network; Machine learning
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-023-02078-1

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. Methods PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC). Results Of 257 citations, 28 publications were included. Random survival forests ( N  = 16, 57%) and neural networks ( N  = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology ( N  = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events ( N  = 27, 96%) in the oncology, while less were used for treatment outcomes ( N  = 1, 4%). Conclusions The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.