ArticleStatistics in medicine2022
Deep learning for the dynamic prediction of multivariate longitudinal and survival data.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.BMC medical research methodology · 2025Pooled it
- A Framework for the Estimation of Quality-Adjusted Life-Years Using Joint Models of Longitudinal and Survival Data.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.BMC medical informatics and decision making · 2026Article
- Reduction techniques for survival analysis.Lifetime data analysis · 2026Article
- Deep Learning Models: A Lens Worth Sharpening for Cancer Prognostication.Annals of surgical oncology · 2026Article
- Multimodal deep learning with missing data robustness for enhanced early diagnosis of coronary artery disease using CCTA, clinical, and ECG data.BMC medical informatics and decision making · 2026Article
- Longitudinal multi-modal data prediction model for mild cognitive impairment by deep survival analysis.BMC medical informatics and decision making · 2026Article
- Deep learning for the joint analysis of item-level longitudinal and survival data.Journal of applied statistics · 2026Article
- JointLIME: An interpretation method for machine learning survival models with endogenous time-varying covariates in credit scoring.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- TransformerLSR: Attentive joint model of longitudinal data, survival, and recurrent events with concurrent latent structure.Artificial intelligence in medicine · 2025Article
- tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Neural Network for Correlated Survival Outcomes Using Frailty Model.Journal of data science : JDS · 2025Article
- A comparative study of methods for dynamic survival analysis.Frontiers in neurology · 2025Article
- Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.NPJ digital medicine · 2024Article
- Article
- A transformer model for cause-specific hazard prediction.BMC bioinformatics · 2024Article
- Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage.Scientific reports · 2024Article
- Deep learning models to predict primary open-angle glaucoma.Stat (International Statistical Institute) · 2024Article
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2 authors.
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Abstract
The joint model for longitudinal and survival data improves time-to-event predictions by including longitudinal outcome variables in addition to baseline covariates. However, in practice, joint models may be limited by parametric assumptions in both the longitudinal and survival submodels. In addition, computational difficulties arise when considering multiple longitudinal outcomes due to the large number of random effects to be integrated out in the full likelihood. In this article, we discuss several recent machine learning methods for incorporating multivariate longitudinal data for time-to-event prediction. The presented methods use functional data analysis or convolutional neural networks to model the longitudinal data, both of which scale well to multiple longitudinal outcomes. In addition, we propose a novel architecture based on the transformer neural network, named TransformerJM, which jointly models longitudinal and time-to-event data. The prognostic abilities of each model are assessed and compared through both simulation and real data analysis on Alzheimer's disease datasets. Specifically, the models were evaluated based on their ability to dynamically update predictions as new longitudinal data becomes available. We showed that TransformerJM improves upon the predictive performance of existing methods across different scenarios.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.