Machine Learning for Multimodal Healthcare Data [electronic resource] : First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings / edited by Andreas K. Maier, Julia A. Schnabel, Pallavi Tiwari, Oliver Stegle.
Contributor(s): Maier, Andreas K [editor.]
| Schnabel, Julia A [editor.] | Tiwari, Pallavi [editor.] | Stegle, Oliver [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 14315Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edition: 1st ed. 2024.Description: X, 190 p. 44 illus., 38 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031476792.Subject(s): Medical informatics | Health InformaticsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 610,285 Online resources: Click here to access online
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Springer Nature eBookSummary: 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.
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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.
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