ArticleFrontiers in cardiovascular medicine2023
Improvement of a prediction model for heart failure survival through explainable artificial intelligence.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 5 of them syntheses that pooled it.
What it found
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Who cites it
21 citing papers in PubMed, 5 syntheses or guidelines pooled it, 3 citations in OpenAlex.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.BMC medical informatics and decision making · 2025Pooled it
- A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Interpretable SHAP-based machine learning framework for patient satisfaction prediction: a case study in Thammasat University Hospital.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Enhanced Prediction of Cardiovascular Disease Through Integrated Machine Learning Models Combining Clinical and Demographic Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Survival machine learning models for predicting all-cause and case-specific mortality risk in metabolic dysfunction-associated fatty liver disease patients.Scientific reports · 2025Article
- AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.Scientific reports · 2025Article
- Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.Scientific reports · 2025Article
- An explainable multi-objective hybrid machine learning model for reducing heart failure mortality.PeerJ. Computer science · 2025Article
- Cognitive alignment in cardiovascular AI: designing predictive models that think with, not just for, clinicians.Frontiers in cardiovascular medicine · 2025Article
- Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors.Frontiers in digital health · 2025Article
- AI-based prediction of heart failure progression in persistent atrial fibrillation using wearable electrocardiography: a brief research report.Frontiers in cardiovascular medicine · 2025Article
- A machine-learned model for predicting weight loss success using weight change features early in treatment.NPJ digital medicine · 2024Article
- Toward explainable deep learning in healthcare through transition matrix and user-friendly features.Frontiers in artificial intelligence · 2024Article
- Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease.Brain informatics · 2023Article
- A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases.The Journal of supercomputing · 2023Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases and their associated disorder of heart failure (HF) are major causes of death globally, making it a priority for doctors to detect and predict their onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnoses and treatments. Specifically, "eXplainable AI" (XAI) offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of two HF survival prediction models using a dataset that includes 299 patients who have experienced HF. The first model utilizes survival analysis, considering death events and time as target features, while the second model approaches the problem as a classification task to predict death. The model employs an optimization data workflow pipeline capable of selecting the best machine learning algorithm as well as the optimal collection of features. Moreover, different
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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.