ArticleNPJ digital medicine2025
Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Structural requirements for intelligent clinical digital twins in feedback-driven care.npj health systems · 2026Review
- Mechanical-medical convergence in heart failure: Artificial intelligence, finite-element modeling, and 3D printing for diagnosis and prognosis.Bioengineering & translational medicine · 2026Review
- Assessing Quality of Life in Genetic Cardiomyopathies: A Scoping Review.International journal of environmental research and public health · 2026Article
- Digital twins and digital models of the human circulatory system.Nature reviews bioengineering · 2026Article
- Cardiovascular digital twins using a Windkessel physics informed neural network.NPJ digital medicine · 2026Article
- Multiscale computational modeling of the cardiopulmonary consequences of postnatal hyperoxia with implications for preterm-born children.Biomechanics and modeling in mechanobiology · 2026Article
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Distress as a bridge to suicidality in schizophrenia spectrum disorders: a network-based intervention simulation study.Research square · 2026Article
- AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration.International journal of molecular sciences · 2026Review
- AI digital-twin ecosystem translating gut-microbiome-neuroimmune signals into precision sleep-mood interventions.Frontiers in psychiatry · 2026Review
- WMRE2030: integrating wearable devices, multi-omics, and artificial intelligence-driven real-time feedback into a daily-scale closed-loop framework for a new era of precision exercise.Frontiers in physiology · 2026Review
- The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics.Heart views : the official journal of the Gulf Heart Association · 2026Review
- Multiscale Computational Modeling of the Cardiopulmonary Consequences of Postnatal Hyperoxia with Implications for Preterm Born Children.bioRxiv : the preprint server for biology · 2025Article
- Multi-sensor wearables re-shaping care of chronic heart-failure: A narrative review.The Indian journal of medical research · 2025Review
- Machine learning-based phenotyping and assessment of treatment responses in heart failure with preserved ejection fraction.EClinicalMedicine · 2025Article
- Artificial Intelligence and Advanced Digital Health for Hypertension: Evolving Tools for Precision Cardiovascular Care.Medicina (Kaunas, Lithuania) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
Heart failure (HF) is a highly heterogeneous condition, and current methods struggle to synthesize extensive clinical data for personalized care. Using data from 343 HF patients, we developed mechanistic computational models of the cardiovascular system to create digital twins. These twins, consisting of optimized measurable and unmeasurable parameters alongside simulations of cardiovascular function, provided comprehensive representations of individual disease states. Unsupervised machine learning applied to digital twin-derived features identified interpretable phenogroups and mechanistic drivers of cardiovascular death risk. Incorporating these features into prognostic AI models improved performance, transferability, and interpretability compared to models using only clinical variables. This framework demonstrates potential to enhance prognosis and guide therapy, paving the way for more precise, individualized HF management.
Identifiers
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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.