Observational studyBMC medical research methodology2019
Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis.
Observational study in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01076660 (Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of), which is not on this map. Cited by 43 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of: Functional Energetics and Imaging for Phenotypic Characterization of Patients at Risk for Sudden Cardiac Death, See Also NCT000181233]
Who cites it
43 citing papers in PubMed, 3 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
- 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
- Article
- 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
- Rforce: Random Forests for Composite Endpoints.Statistics in medicine · 2026Article
- Genomic risk model to implement precision prostate cancer screening in clinical care: the ProGRESS study.Nature cancer · 2026Article
- Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke Within the UK Biobank.Healthcare (Basel, Switzerland) · 2025Article
- Individual Heterogeneity and Trends in Hepatitis C Infection Risk Among People Who Inject Drugs: A Longitudinal Analysis.Journal of viral hepatitis · 2025Article
- Predicting survival in small cell lung cancer patients undergoing various treatments: a machine learning approach.Translational lung cancer research · 2025Article
- Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data.BMC medical research methodology · 2025Article
- Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review.Frontiers in oral health · 2025Review
- Article
- Application of machine learning in breast cancer survival prediction using a multimethod approach.Scientific reports · 2024Article
- An inverse lomax-uniform poisson distribution and joint modeling of repeatedly measured and time-to-event data in the health sectors.Scientific reports · 2024Article
- Development of an AI-Based Predictive Algorithm for Early Diagnosis of High-Risk Dementia Groups among the Elderly: Utilizing Health Lifelog Data.Healthcare (Basel, Switzerland) · 2024Article
- Interpretable artificial intelligence to optimise use of imatinib after resection in patients with localised gastrointestinal stromal tumours: an observational cohort study.The Lancet. Oncology · 2024Observational
- Explainable text-tabular models for predicting mortality risk in companion animals.Scientific reports · 2024Article
- New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model.Cancer immunology, immunotherapy : CII · 2024Article
- Establishment of a model to predict mortality after decompression craniotomy for traumatic brain injury.Brain and behavior · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundClinical research and medical practice can be advanced through the prediction of an individual's health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that are static, rather than dynamic. To benefit from the increasing emergence of large, heterogeneous data sets, such as electronic health records (EHRs), novel tools to support improved clinical decision making through methods for individual-level risk prediction that can handle multiple variables, their interactions, and time-varying values are necessary.
methodsWe introduce a novel dynamic approach to clinical risk prediction for survival, longitudinal, and multivariate (SLAM) outcomes, called random forest for SLAM data analysis (RF-SLAM). RF-SLAM is a continuous-time, random forest method for survival analysis that combines the strengths of existing statistical and machine learning methods to produce individualized Bayes estimates of piecewise-constant hazard rates. We also present a method-agnostic approach for time-varying evaluation of model performance.
resultsWe derive and illustrate the method by predicting sudden cardiac arrest (SCA) in the Left Ventricular Structural (LV) Predictors of Sudden Cardiac Death (SCD) Registry. We demonstrate superior performance relative to standard random forest methods for survival data. We illustrate the importance of the number of preceding heart failure hospitalizations as a time-dependent predictor in SCA risk assessment.
conclusionsRF-SLAM is a novel statistical and machine learning method that improves risk prediction by incorporating time-varying information and accommodating a large number of predictors, their interactions, and missing values. RF-SLAM is designed to easily extend to simultaneous predictions of multiple, possibly competing, events and/or repeated measurements of discrete or continuous variables over time.
trial registrationLV Structural Predictors of SCD Registry (clinicaltrials.gov, NCT01076660), retrospectively registered 25 February 2010.
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Registered trials
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.