ArticleFrontiers in cardiovascular medicine2021
Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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
15 citing papers in PubMed, 20 citations in OpenAlex.
- Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data.Orphanet journal of rare diseases · 2023Trial
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- Adverse Drug Reactions in Children with Congenital Heart Disease: A Scoping Review.Paediatric drugs · 2024Article
- CHDmap: One Step Further Toward Integrating Medicine-Based Evidence Into Practice.JMIR medical informatics · 2024Article
- Second harvest of Congenital Heart Surgery Database in Türkiye: Current outcomes.Turk gogus kalp damar cerrahisi dergisi · 2024Article
- A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study.JMIR medical informatics · 2024Article
- Automated prediction of cardiorespiratory deterioration in patients with single-ventricle parallel circulation: A multicenter validation study.JTCVS open · 2023Article
- Charting the Path: Navigating Embryonic Development to Potentially Safeguard against Congenital Heart Defects.Journal of personalized medicine · 2023Review
- Review
- Artificial Intelligence in Congenital Heart Disease: Current State and Prospects.JACC. Advances · 2022Review
- Artificial Intelligence in Pediatric Cardiology: A Scoping Review.Journal of clinical medicine · 2022Article
- Big Data in Cardiology: State-of-Art and Future Prospects.Frontiers in cardiovascular medicine · 2022Review
- The horizon of pediatric cardiac critical care.Frontiers in pediatrics · 2022Review
- Artificial intelligence in pediatric cardiology: Where do we stand in 2024?Annals of pediatric cardiologyArticle
- The paucity of high-level evidence for therapy in pediatric cardiology.Annals of pediatric cardiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Built on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individually, each may differ in important ways from an RCT cohort, and the physician will wonder each time if following EBM will provide best guidance for this unique patient. In an effort to overcome this weakness, and promote higher quality care through a more personalized approach, a new framework has been proposed: Medicine-Based Evidence (MBE). In this approach, big data and deep learning techniques are embraced to interrogate treatment responses among patients in real-world clinical practice. Such statistical models are then integrated with mechanistic disease models to construct a "digital twin," which serves as the real-time digital counterpart of a patient. MBE is thereby capable of dynamically modeling the effects of various treatment decisions in the context of an individual's specific characteristics. In this article, we discuss how MBE could benefit patients with congenital heart disease, a field where RCTs are difficult to conduct and often fail to provide definitive solutions because of a small number of subjects, their clinical complexity, and heterogeneity. We will also highlight the challenges that must be addressed before MBE can be embraced in clinical practice and its full potential can be realized.
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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.