{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:00:58Z","timestamp":1786978858393,"version":"build-2736575974"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Early identification of COVID-19 patients requiring intensive care is critical for improving treatment prioritization, supporting clinical decision-making, and managing limited hospital resources. While structured electronic health record (EHR) data provide important physiological information, chest computed tomography (CT) imaging contains additional indicators related to disease severity. This study presents a multimodal clinical decision support framework for a multimodal risk prediction framework for severe COVID-19 outcomes using structured emergency clinical features and patient-level CT imaging from the COVID Data for Shared Learning (CDSL) dataset. After multimodal cohort construction, 784 patients with both structured clinical records and CT imaging were included in the analysis. Three predictive settings were evaluated: EHR-only prediction using Gradient Boosting, CT-only prediction using ResNet50-based feature extraction with Logistic Regression, and multimodal prediction using weighted late fusion. The experimental results indicate that the CT-based model surpassed the clinical baseline, yielding an F1-score of 0.42 and an ROC-AUC of 0.772, whereas the EHR-only model achieved scores of 0.30 and 0.715, respectively. Overall, the multimodal fusion framework achieved the strongest results among the approaches tested, reaching an F1-score of 0.47 and an ROC-AUC of 0.782. Taken together, these findings indicate that, although CT imaging alone carries meaningful predictive power for evaluating ICU risk, combining it with clinical data leads to predictions that are more reliable and robust. The proposed framework offers a practical and interpretable foundation for multimodal clinical decision support and demonstrates the potential of combining structured clinical data with medical imaging for intelligent critical care applications.<\/jats:p>","DOI":"10.3390\/bioengineering13080929","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T13:42:17Z","timestamp":1786974137000},"page":"929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evidence-Guided Multimodal Risk Prediction Framework for Severe COVID-19 Outcomes Using EHR and CT Imaging for COVID-19 Clinical Decision Support"],"prefix":"10.3390","volume":"13","author":[{"given":"Muhammad Zohaib","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Muhammad Ali Jinnah University, Karachi 75400, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3660-065X","authenticated-orcid":false,"given":"Shaukat","family":"Wasi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Muhammad Ali Jinnah University, Karachi 75400, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5656-0416","authenticated-orcid":false,"given":"Muhammad Shoaib","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0077-9638","authenticated-orcid":false,"given":"Ghufran","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, National University of Computer and Emerging Sciences (FAST-NUCES), Karachi 75030, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2035-7205","authenticated-orcid":false,"given":"Muhammad Hussain","family":"Mughal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sukkur IBA University, Sukkur 65020, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsin","family":"Iftikhar","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Science, Higher Colleges of Technology, Fujairah P.O. Box 25026, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1038\/s41467-020-20816-7","article-title":"Real-time prediction of COVID-19 related mortality using electronic health records","volume":"12","author":"Schwab","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e30157","DOI":"10.2196\/30157","article-title":"COVID-19 Mortality Prediction from Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation","volume":"23","author":"Sankaranarayanan","year":"2021","journal-title":"J. Med. 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