ArticleScientific reports2023
Predicting heart failure onset in the general population using a novel data-mining artificial intelligence method.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- An interpretable combinatorial data-mining framework for predicting new-onset hypertension in the general population.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis.Circulation research · 2025Review
- Upgrading COCATS: A Fellow's Perspective on Practical AI Integration With Cardiovascular Fellowship Training.JACC. Advances · 2024Article
- Machine learning-based classification of valvular heart disease using cardiovascular risk factors.Scientific reports · 2024Article
- Early detection of heart failure using in-patient longitudinal electronic health records.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to identify combinations of clinical factors that predict heart failure (HF) onset using a novel limitless-arity multiple-testing procedure (LAMP). We also determined if increases in numbers of predictive combinations of factors increases the probability of developing HF. We recruited people without HF who received health check-ups in 2010, who were followed annually for 4 years. Using 32,547 people, LAMP was performed to identify combinations of factors of fewer than four factors that could predict the onset of HF. The ability of the method to predict the probability of HF onset based on the number of matching predictive combinations of factors was determined in 275,658 people. We identified 549 combinations of factors for the onset of HF. Then we classified 275,658 people into six groups who had 0, 1-50, 51-100, 101-150, 151-200 or 201-250 predictive combinations of factors for the onset of HF. We found that the probability of HF progressively increased as the number of predictive combinations of factors increased. We identified combinations of variables that predict HF onset. An increased number of matching predictive combinations for the onset of HF increased the probability of HF onset.
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Registered trials
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