ArticleNPJ digital medicine2025
Artificial intelligence-driven multivariate integration for pulmonary arterial pressure prediction in pulmonary hypertension.
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 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Artificial Intelligence and Multi-Omics Approaches in the Precision Management of Pulmonary Hypertension: From Early Diagnosis to Therapeutic Stratification.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- Clinical research on pulmonary hypertension from a 2025 perspective: a narrative review.Journal of thoracic disease · 2026Review
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Reliable machine learning techniques have vast potential in assisting clinical decision-making, including applications in bioinformatics and medical imaging analysis. However, AI-driven medical research is often limited by data scarcity, data quality, and the black-box nature of machine learning models. Thus, there is an urgent need for reliable surrogate models to overcome these challenges, enabling accurate learning from small datasets to guide clinical diagnosis. Here, we conducted a retrospective observational clinical study and proposed a data-driven predictive model that estimates mean pulmonary artery pressure (mPAP) based on individual patient clinical diagnostic features, enabling accurate assessment of pulmonary hypertension. Furthermore, we innovatively incorporate CMR-related features into the disease evaluation framework. Compared to traditional invasive measurement methods, this framework can not only accurately predict a patient's mPAP using easily accessible noninvasive physiological features but also incorporate uncertainty quantification to extract qualitative patterns, aiding clinical diagnosis.
Identifiers
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.