ArticleNPJ digital medicine2023
An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Cluster-Based Phenotyping in an HFpEF Population Identifies a Biomarker-High, Atrial-Dysfunction Phenotype with Impaired Quality of Life, Reduced Exercise Capacity and Higher Healthcare Utilization.Journal of clinical medicine · 2026Article
- The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.Current heart failure reports · 2026Review
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- The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026Review
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- Artificial Intelligence in Pharmaceutical Drug Development: Transforming Formulation and Innovation.Current drug discovery technologies · 2026Review
- Solving the 'Goldilocks problem' in dementia clinical trials with multimodal AI.The journal of prevention of Alzheimer's disease · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
- Big Data Analytics in Large Cohorts: Opportunities and Challenges for Research in Hepatology.Seminars in liver disease · 2025Review
- Review
- Artificial Intelligence-Enabled Clinical Trials in Inflammatory Bowel Disease: Automating and Enhancing Disease Assessment and Study Management.Gastroenterology · 2025Review
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Computational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference.Circulation. Cardiovascular quality and outcomes · 2025Article
- The promises and limitations of artificial intelligence for quality improvement, patient safety, and research in hospital medicine.Journal of hospital medicine · 2025Article
- Artificial intelligence in oncology drug development and management: a precision medicine perspective.Frontiers in oncology · 2025Review
- Cardiovascular care with digital twin technology in the era of generative artificial intelligence.European heart journal · 2024Review
- Artificial Intelligence in Cardiovascular Clinical Trials.Journal of the American College of Cardiology · 2024Review
- Measures to Improve Trial Enrollment: It's Game Time!JACC. Heart failure · 2024Article
- Clinical trial design, end-points, and emerging therapies in pulmonary arterial hypertension.The European respiratory journal · 2024Review
Corrections and comments
- Update of
Authors and funding
8 authors.
Funding
Abstract
Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, p
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What 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.