{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:06:08Z","timestamp":1776121568631,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions. Moreover, concerns around data security and privacy continue to challenge the widespread adoption of these models. To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. The application achieved an accuracy of 98.28% for brain stroke prediction on the selected dataset. The data security was enhanced by integrating consortium blockchain technology with machine learning. The results show that the application is tamper-proof and is capable of detecting and automatically correcting backend data anomalies to maintain robust data protection. The application can be extended to monitor other pathologies such as heart attacks, cancers, osteoporosis, and epilepsy with minimal configuration changes.<\/jats:p>","DOI":"10.1186\/s40708-024-00247-6","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T15:55:54Z","timestamp":1736870154000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Blockchain-enabled digital twin system for brain stroke prediction"],"prefix":"10.1186","volume":"12","author":[{"given":"Venkatesh","family":"Upadrista","sequence":"first","affiliation":[]},{"given":"Sajid","family":"Nazir","sequence":"additional","affiliation":[]},{"given":"Huaglory","family":"Tianfield","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"247_CR1","doi-asserted-by":"crossref","unstructured":"Vats T, Singh SK, Kumar S, Gupta BB, Gill SS, Arya V, Alhalabi W (2023) Explainable context-aware IoT framework using human digital twin for healthcare, Explainable Artificial Intelligence Solutions for In-the-wild Human Behavior Analysis.","DOI":"10.1007\/s11042-023-16922-5"},{"key":"247_CR2","unstructured":"Okegbile SD, Cai J, Yi C, Niyato D (2022) Human Digital Twin for Personalized Healthcare: Vision, Architecture and future directions. IEEE Network, pp. 1\u20137"},{"key":"247_CR3","unstructured":"Azzaoui AE, Kim TW, Loia V, Park J (2020) Blockchain-Based Secure Digital Twin Framework for Smart Healthy City. Adv Multimedia Ubiquitous Eng., Dec"},{"key":"247_CR4","doi-asserted-by":"crossref","unstructured":"Chakshu NK, Carson J, Sazonov I, Nithiarasu P (2019) A semi-active human digital twin model for detecting severity of carotid stenoses from head vibration - a coupled computational mechanics and computer vision method: a semi-active human digital twin model for detecting carotid stenoses. Int J Numer Methods Biomed Eng.","DOI":"10.1002\/cnm.3180"},{"key":"247_CR5","doi-asserted-by":"crossref","unstructured":"He DD, Winokur ES, Sodini CG (2011) A continuous, wearable, and wireless heart monitor using head ballistocardiogram (BCG) and head electrocardiogram (ECG). 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