ArticleBioinformatics (Oxford, England)2023
survex: an R package for explaining machine learning survival models.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Development and validation of a machine learning clinicogenomic model to improve prognostic stratification in ER-positive/HER2-negative early breast cancer.Breast cancer research : BCR · 2026Article
- Comprehensive Pan-Cancer Analysis of ABCA1: Insights From Multi-Omics Data and Exploratory Validation in Esophageal Squamous Cell Carcinoma.Cell biochemistry and function · 2026Article
- Growth differentiation factor-15 and the incidence, bidirectional progression, and risk prediction of atherosclerotic cardiovascular disease and metabolic dysfunction-associated steatotic liver disease in individuals with cardiovascular-kidney-metabolic syndrome stages 0-3.Cardiovascular diabetology · 2026Article
- Integrating single-cell multi-omics and machine learning to reveal triaptosis heterogeneity in clear cell renal cell carcinoma.Human genomics · 2026Article
- Machine learning survival model for personalised prevention of catheter-related thrombosis in tumour patients.Communications medicine · 2026Article
- Machine learning prediction of tuberculosis mortality: a comparative analysis of random survival forest and cox regression models.BMC infectious diseases · 2026Article
- Clinicogenomic Insights for Progression-Free Survival in Prostate Cancer.International journal of environmental research and public health · 2026Article
- Comprehensive analysis of key palmitoylation-modifying enzymes in clear cell renal cell carcinoma: implications for prognosis and therapy.Cancer cell international · 2026Article
- Development and comparative evaluation of machine learning algorithms and Cox regression for predicting fasting plasma glucose-defined incident prediabetes: a longitudinal cohort study.Frontiers in endocrinology · 2026Article
- Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark-Based Machine Learning.Scientifica · 2026Article
- Longitudinal CT-based deep learning radiomics for predicting prognosis in esophageal squamous cell carcinoma treated with definitive chemoradiotherapy: a two-center study.Journal of Cancer · 2026Article
- Article
- Multimodal Data-Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure With Preserved Ejection Fraction: Multicenter, Cross-Regional Cohort Study.Journal of medical Internet research · 2025Article
- Prognostic value of brown adipocyte-related genes in colorectal cancer: a multi-omics and Mendelian randomization study.Discover oncology · 2025Article
- Interpretable Machine Learning for Survival Analysis.Biometrical journal. Biometrische Zeitschrift · 2025Review
- Integrative Single-Cell and Machine Learning Analysis Develops a Glutamine Metabolism-Based Prognostic Model and Identifies MSMO1 as a Therapeutic Target in Osteosarcoma.Biomolecules · 2025Article
- Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event.BMC medical research methodology · 2025Article
- Explainable models for predicting long-term outcomes in patients with spontaneous intracerebral haemorrhage: a retrospective cohort study.Stroke and vascular neurology · 2025Article
- Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction.BMC gastroenterology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
summaryDue to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications. AVAILABILITY AND IMPLEMENTATION: survex is available under the GPL3 public license at https://github.com/modeloriented/survex and on CRAN with documentation available at https://modeloriented.github.io/survex.
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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.