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Titlebook: Machine Learning for Multimodal Healthcare Data; First International Andreas K. Maier,Julia A. Schnabel,Oliver Stegle Conference proceedin

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書(shū)目名稱Machine Learning for Multimodal Healthcare Data
副標(biāo)題First International
編輯Andreas K. Maier,Julia A. Schnabel,Oliver Stegle
視頻videohttp://file.papertrans.cn/621/620635/620635.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Machine Learning for Multimodal Healthcare Data; First International  Andreas K. Maier,Julia A. Schnabel,Oliver Stegle Conference proceedin
描述This book constitutes the proceedings of the First International Workshop on Machine Learning for Multimodal Healthcare Date, ML4MHD 2023, held in Honolulu, Hawaii, USA, in July 2023.?.The 18 full papers presented were carefully reviewed and selected from 30 submissions. The workshop‘s primary objective was to bring together experts from diverse fields such as medicine, pathology, biology, and machine learning. With the aim to present novel methods and solutions that address healthcare challenges, especially those that arise from the complexity and heterogeneity of patient data..
出版日期Conference proceedings 2024
關(guān)鍵詞Computer Science; Informatics; Conference Proceedings; Research; Applications
版次1
doihttps://doi.org/10.1007/978-3-031-47679-2
isbn_softcover978-3-031-47678-5
isbn_ebook978-3-031-47679-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Neural Graph Revealers,stic queries. This limits their adoption to only identifying connections among domain variables. On the other hand, Probabilistic Graphical Models (PGMs) learn an underlying base graph together with a distribution over the variables (nodes). PGM design choices are carefully made such that the infere
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,Multi-modal Biomarker Extraction Framework for?Therapy Monitoring of?Social Anxiety and?Depression from social anxiety or depression. It operates multi-modal (decision fusion) by incorporating audio and video recordings of a patient and the corresponding interviewer, at two separate test assessment sessions. The used data is provided by an ongoing project in a day-hospital and outpatient setting
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,Semi-supervised Cooperative Learning for?Multiomics Data Fusion,ical systems and enhance predictions on outcomes of interest related to disease phenotypes and treatment responses. Cooperative learning, a recently proposed method, unifies the commonly-used fusion approaches, including early and late fusion, and offers a systematic framework for leveraging the sha
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