ArticleScientific reports2023
Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for identifying medication overuse or medication overuse headache in migraine patients: a systematic review.The journal of headache and pain · 2024Pooled it
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- Development of a prediction model for infant hospitalisation and death using clinical features assessed by community health workers during routine postnatal home visits in Dhaka, Bangladesh.BMJ paediatrics open · 2026Article
- Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study.JMIR cancer · 2026Article
- Random Survival Forest Versus Elastic-Net Regularized Cox Regression for Survival Prediction in Acute Myeloid Leukemia at Distinct Treatment Time Points: Model Performance Comparison Study.JMIR bioinformatics and biotechnology · 2026Article
- A time-adapted risk stratification framework guided by longitudinal post-radiotherapy EBV DNA clearance for high-risk nasopharyngeal carcinoma patients.Radiation oncology (London, England) · 2026Article
- Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet.Lifetime data analysis · 2026Article
- Prediction of First and Multiple Antiretroviral Therapy Interruptions in People Living With HIV: Comparative Survival Analysis Using Cox and Explainable Machine Learning Models.JMIR medical informatics · 2026Article
- Survival Machine Learning Methods Improve Prediction of Histologic Transformation in Follicular and Marginal Zone Lymphomas.Cancers · 2025Article
- Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.Scientific reports · 2025Article
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- Establishing a prognostic scoring system and exploring prognostic value of examined lymph node numbers for stage I non-small cell lung cancer: a retrospective study of Surveillance, Epidemiology, and End Results (SEER) database and a Chinese cohort.Translational cancer research · 2025Article
- Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer.Journal of gastrointestinal oncology · 2024Article
- Prediction model for survival of younger patients with breast cancer using the breast cancer public staging database.Scientific reports · 2024Article
- Precision phenotyping from routine laboratory parameters for out of hospital survival prediction in an all comers prospective PCI registry.Scientific reports · 2024Article
- Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties.BMC medical education · 2024Article
- Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer.Scientific reports · 2024Article
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4 authors.
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Abstract
The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods-gradient boosting model (gbm), random survival forest (rsf), elastic net (enet), lasso and ridge-were compared to the traditional Cox proportional hazards (coxph) model and the prior study which used the yearly-cohort-based time-invariant analysis. Using Harrell's C index as our primary measure, we found that using both machine learning techniques and incorporating time-dependent covariates can improve predictive performance. Gradient boosting machine showed the best performance on test data in both time-invariant and time-varying covariates analysis.
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