{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T11:11:02Z","timestamp":1791544262193,"version":"4.3.4"},"reference-count":46,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["European J of Haematology"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included \u201cartificial intelligence\u201d and \u201cvenous thromboembolism.\u201d Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 20 studies were included. Number of participants ranged from 31 to 111\u2009888. The AI\u2010based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74\u20130.85) versus 0.61 (95% CI: 0.54\u20130.68), respectively (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ejh.14110","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T15:10:05Z","timestamp":1696518605000},"page":"951-962","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis"],"prefix":"10.1111","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0443-1751","authenticated-orcid":false,"given":"Thita","family":"Chiasakul","sequence":"first","affiliation":[{"name":"Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of Hematology, Faculty of Medicine, Department of Medicine, Center of Excellence in Translational Hematology Chulalongkorn University and King Chulalongkorn Memorial Hospital  Bangkok Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5585-3823","authenticated-orcid":false,"given":"Barbara D.","family":"Lam","sequence":"additional","affiliation":[{"name":"Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan","family":"McNichol","sequence":"additional","affiliation":[{"name":"Division of Knowledge Services, Department of Information Services (M.M.) 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