{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T06:23:42Z","timestamp":1787207022047,"version":"build-2736575974"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cardiovascular risk prediction remains a difficult problem due to the extreme class imbalance, time sparsity, non-uniform sampling, and heterogeneity of electronic health record and wearable sensor streams in the real world.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multimodal cardiovascular risk prediction framework called Quantum-Optimized Spatio-Temporal Transformer (QST-Trans) was designed. The framework combines Contextual Dual-Stream Synchronization to facilitate alignment between irregular clinical and wearable measurements, Enhanced Quantum-Inspired Binary Grey Wolf Optimization to select features with feature redundancy, a spatio-temporal transformer including attention gating and Kolmogorov-Arnold Network (KAN) layers, and time-aware SHAP to interpret the model's predictions at the patient level based on their longitudinal risk courses. The MIMIC-III and CAIR-CVD-2025 and UCI Heart Disease data sets were used to evaluate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed QST-Trans framework successfully achieved AUC-ROC 0.99, F1-score 0.98, and sensitivity 97.5%, outperforming the considered baseline methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results suggest that multimodal synchronization, quantum-inspired feature optimization, spatio-temporal learning and the inclusion of time-aware explainability are a promising approach to cardiovascular risk stratification. But a multi-center validation and clinical testing in the real world should be required before clinical adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1824194","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T05:26:40Z","timestamp":1787203600000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum-optimized spatio-temporal transformer for multimodal cardiovascular risk prediction from clinical and wearable data"],"prefix":"10.3389","volume":"13","author":[{"given":"S.","family":"Sathya","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology","place":["Coimbatore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Saranya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bannari Amman Institute of Technology","place":["Sathyamangalam, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/life15010094","article-title":"Transforming cardiovascular risk prediction: a review of machine learning and artificial intelligence innovations","volume":"15","author":"Kasartzian","year":"2025","journal-title":"Life"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.5784022","article-title":"Deep learning-driven early detection and risk prediction of chronic diseases using multimodal healthcare data","year":"2025","journal-title":"SSRN"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1007\/s11831-024-10194-4","article-title":"A comprehensive review on heart disease risk prediction using machine learning and deep learning algorithms","volume":"32","author":"Karna","year":"2025","journal-title":"Arch Comput Methods Eng"},{"key":"B4","doi-asserted-by":"publisher","first-page":"pbaf016","DOI":"10.1093\/pcmedi\/pbaf016","article-title":"Utilizing multimodal artificial intelligence to advance diagnostic performance in ICU settings","volume":"8","author":"Yang","year":"2025","journal-title":"Precis Clin Med"},{"key":"B5","doi-asserted-by":"publisher","first-page":"102803","DOI":"10.1016\/j.artmed.2024.102803","article-title":"Artificial intelligence methods in cardiac disease prediction: a comprehensive survey","volume":"150","author":"El Habib Daho","year":"2025","journal-title":"Artif Intell Med"},{"key":"B6","doi-asserted-by":"publisher","first-page":"34567","DOI":"10.1109\/ACCESS.2025.3349127","article-title":"Multimodal data imbalance challenges in clinical risk prediction","volume":"13","author":"Akter","year":"2025","journal-title":"IEEE Access"},{"key":"B7","doi-asserted-by":"publisher","first-page":"65","DOI":"10.61882\/ist.202502.07.06","article-title":"A systematic review of machine-learning models for cardiovascular risk prediction","volume":"2","year":"","journal-title":"InfoSci. 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