Titlebook: International Conference on Biomedical and Health Informatics 2022; Proceedings of ICBHI Esteban Pino,Ratko Magjarevi?,Paulo de Carvalho Co

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Evaluating the?Social Media Users’ Mental Health Status During COVID-19 Pandemic Using Deep Learningvities like. For text, we found that the topics and general sentiment were always depressive and with negative connotation, however, during pre-pandemic they described attributes of the symptomatology of depression pathology, while in post-pandemic are more related to the product of isolation and fe

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A System Detection of?Atrial Fibrillation Using One ECG Derivation and?Inductive Transfer Learningrces. In this work, we employ the Inductive Transfer Learning (ITL) method and a ResNet18. This network is pre-trained with the public icentia11k database in a beat classification job. The fine-tuning stage was performed by merging the PhysioNet data challenges (2020, 2021) to configure a single dat

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Cardiovascular Risk Assessment: An Interpretable Machine Learning Approach Three main steps can be identified: i) derivation of an initial set of rules directly from current clinical evidence and/or data, ii) personalized scheme where a subset of the initial rules is identified as the most adequate one to classify a given patient; iii) an ensemble voting strategy based on

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VocES – An Open Database of Child and Youth Vowels in Spanish for Research Purposesse and relates some research applications. VocES contains 1,665 recordings of the five vowels of Spanish from subjects aged 3 to 18, and also includes information on the age, height, and gender of the speakers.

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REHASTART: Cognitive Tele-Rehabilitation Empowered by?Vision Transformersdard clinical routine, it is necessary to strictly follow rehabilitation exercises to prevent them from being ineffective..Given these premises, the REHASTART project proposes a platform based on a deep learning framework for monitoring patients’ engagement through automated classification of facial

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