ArticleNature communications2024
AI hybrid survival assessment for advanced heart failure patients with renal dysfunction.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.Journal of translational medicine · 2025Pooled it
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- From Disease-Specific Models to Broad Clinical Utility: A Perspective on AI Hybrid Ensemble Frameworks.Advanced genetics (Hoboken, N.J.) · 2026Article
- CardioEHR: A longitudinal electronic health record dataset of cardiovascular patients from central China.Scientific data · 2026Article
- Multi-omics integration study of vascular smooth muscle cell phenotypic conversion identified novel biomarkers in idiopathic pulmonary arterial hypertension.Respiratory research · 2026Article
- Article
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
- Machine learning combined with omics-based approaches reveals T-lymphocyte cellular fate imbalance in abdominal aortic aneurysm.BMC biology · 2025Article
- Development of a machine learning-based depression risk identification tool for older adults with asthma.BMC psychiatry · 2025Article
- AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen.Reviews in cardiovascular medicine · 2025Article
- A physics-informed and data-driven framework for robotic welding in manufacturing.Nature communications · 2025Article
- Allostatic load-cardiovascular disease associations and the mediating effect of inflammatory factors: a prospective cohort study.Frontiers in cardiovascular medicine · 2025Article
- Association of platelet-to-lymphocyte ratio with 1-year all-cause mortality in ICU patients with heart failure.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system's robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.
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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.