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Subjects: Atrial fibrillation; Machine learning; deep learning
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Source:
Isaksen , J L , Arildsen , B , Lind , C , Nørregaard , M , Vernooy , K , Schotten , U , Jespersen , T , Betz , K , Hermans , A N L , Kanters , J K & Linz , D 2025 , ' Raw photoplethysmogram waveforms
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Subjects: Artificial intelligence; Cluster analysis; Echocardiography
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Simonsen , J Ø , Modin , D , Skaarup , K , Djernæs , K , Lassen , M C H , Johansen , N D , Marott , J L , Jensen , M T , Jensen , G B , Schnohr , P , Martínez , S S , Claggett , B L , Møgelvang ,
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Subjects: Animals; Zebrafish/genetics; CRISPR-Cas Systems
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Source:
Mazzaferro , E , Mujica , E , Zhang , H , Emmanouilidou , A , Jenseit , A , Evcimen , B , Metzendorf , C , Dethlefsen , O , Loos , R J , Vienberg , S G , Larsson , A , Allalou , A & den Hoed , M 2025
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Subjects: artificial intelligence; deep learning; dermatology
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Thomsen , K , Jalaboi , R , Winther , O , Lomholt , H B , Lorentzen , H F , Høgsberg , T , Egekvist , H , Hedelund , L , Jørgensen , S , Frost , S , Bertelsen , T & Iversen , L 2025 , ' Physician
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Subjects: Chest CT; Deep learning; Lung nodule risk estimation
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Source:
Peeters , D , Venkadesh , K V , Dinnessen , R , Saghir , Z , Scholten , E T , Vliegenthart , R , Prokop , M & Jacobs , C 2025 , ' Towards safe and reliable deep learning
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Subjects: Alpha thalassemia; Artificial intelligence; Classification
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Christensen , F , Kılıç , D K , Nielsen , I E , El-Galaly , T C , Glenthøj , A , Helby , J , Frederiksen , H , Möller , S & Fuglkjær , A D 2025 , ' Classification of α-thalassemia data using
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Subjects: Community detection; Educational data mining; Learning analytics
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Source:
Hernández-García , Á , Cuenca-Enrique , C , Traxler , A , López-Pernas , S , Conde-González , M Á & Saqr , M 2024 , Community Detection in Learning Networks Using
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Subjects: Deep neural networks; Generalization error; Learning risk
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Source:
Deng , L , Yang , B , Kang , Z , Wu , J , Li , S & Xiang , Y 2024 , ' Separating hard clean samples from noisy samples with samples’ learning risk for DNN when
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