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Subjects: maintenance; extramedullary disease; myeloma
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Source:
Gagelmann , N , Eikema , D-J , Koster , L , Netelenbos , T , McDonald , A , Stoppa , A-M , Fenk , R , Anagnostopoulos , A , van Gorkom , G , Deconinck , E , Bulabois , C-E , Delforge , M , Bunjes , D
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Subjects: Convolutional Neural Network; Deep Learning; Empirical Mode Decomposition
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Source:
Simon , E , Bonizzi , P , Ntagiou , E , Siminski , J & Cordelli , E 2023 , ' Resident Space Object Classification from Light Curves with Deep Learning ' , Proceedings of the International
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Subjects: Cardiac arrest; Resuscitation; Cognitive impairments
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Source:
Moulaert , V R M , van Heugten , C M , Winkens , B , Bakx , W G M , de Krom , M C F T M , Gorgels , T P M , Wade , D T & Verbunt , J A 2015 , ' Early neurologically-focused follow-up after cardiac
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Subjects: Public health surveillance; Syndromic surveillance; Influenza
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Source:
Rosenkotter , N , Ziemann , A , Garcia-Castrillo Riesgo , L , Gillet , J-B , Vergeiner , G , Krafft , T & Brand , H 2013 , ' Validity and timeliness of syndromic influenza surveillance during the
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Subjects: Artificial Intelligence; Cardiopulmonary Resuscitation/methods; Denmark/epidemiology
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Source:
Scholz , M L , Collatz-Christensen , H , Blomberg , S N F , Boebel , S , Verhoeven , J & Krafft , T 2022 , ' Artificial intelligence in Emergency Medical Services dispatching : assessing the potential
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