ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
Predicting survival time for critically ill patients with heart failure using conformalized survival analysis.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Digital health technology burden and frustration among patients with multimorbidity.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Foundation models for cardiovascular diseases and medicine.European heart journal. Digital health · 2026Review
- Returning value to communities from the All of Us Research Program through the lens of social determinants of health and ethical, legal, and social implications.International journal for equity in health · 2026Review
- Identifying Digital Health Technology Engagement and Frustration Phenotypes in Patients with Multiple Chronic Conditions: Evidences from a Nationally Representative Cross-Sectional Study.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Machine learning-based mortality prediction in critically ill patients with hypertension: comparative analysis, fairness, and interpretability.Frontiers in artificial intelligence · 2025Article
- The collaborations among healthcare systems, research institutions, and industry on artificial intelligence research and development.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Heart failure (HF) is a significant public health challenge, especially among critically ill patients in intensive care units (ICUs). Predicting survival outcomes for these patients with calibrated uncertainty is both challenging and essential for guiding subsequent treatments. This study introduces conformalized survival analysis (CSA) as a novel method for predicting survival times in critically ill HF patients. CSA enhances each predicted survival time with a statistically rigorous lower bound, providing valuable uncertainty quantification. Using the MIMIC-IV dataset, we demonstrate that CSA effectively delivers calibrated uncertainty quantification for survival predictions, in contrast to parametric models like the Cox or Accelerated Failure Time models. Through the application of CSA to a large, real-world dataset, this study underscores its potential to improve decision-making in critical care, offering a more precise and reliable tool for prognosis in a setting where accurate predictions and calibrated uncertainty can profoundly impact patient outcomes.
Indexed as
Identifiers
40502254PMC12150701What OpenQuestion holds
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.