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Machine Learning Models for Predicting Bleeding Risk in Anticoagulated Patients with Atrial Fibrillation and Venous Thromboembolism: A Comparative Evidence Synthesis.

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Title: Machine Learning Models for Predicting Bleeding Risk in Anticoagulated Patients with Atrial Fibrillation and Venous Thromboembolism: A Comparative Evidence Synthesis.
Authors: Teo, Winnie Z. Y.1,2,3,4 (AUTHOR), Wong, Maggie Wing Yin1,2,3,4 (AUTHOR), Lim, Fang Jin2,3 (AUTHOR), Ong, Emmeliene Su-Min2,3,4 (AUTHOR), Barr Kumarakulasinghe, Nesaretnam1,5 (AUTHOR), Yap, Eng Soo1,3,5 (AUTHOR) eng_soo_yap@nuhs.edu.sg
Source: Journal of Clinical Medicine. Mar2026, Vol. 15 Issue 6, p2370. 14p.
Database: Academic Search Ultimate
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  Data: Machine Learning Models for Predicting Bleeding Risk in Anticoagulated Patients with Atrial Fibrillation and Venous Thromboembolism: A Comparative Evidence Synthesis.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Clinical+Medicine%22">Journal of Clinical Medicine</searchLink>. Mar2026, Vol. 15 Issue 6, p2370. 14p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=192624637
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        Value: 10.3390/jcm15062370
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        Text: English
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        StartPage: 2370
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      – TitleFull: Machine Learning Models for Predicting Bleeding Risk in Anticoagulated Patients with Atrial Fibrillation and Venous Thromboembolism: A Comparative Evidence Synthesis.
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            NameFull: Teo, Winnie Z. Y.
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            NameFull: Wong, Maggie Wing Yin
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            NameFull: Lim, Fang Jin
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            NameFull: Ong, Emmeliene Su-Min
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            NameFull: Barr Kumarakulasinghe, Nesaretnam
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              Text: Mar2026
              Type: published
              Y: 2026
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            – TitleFull: Journal of Clinical Medicine
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