Machine Learning for Multimodal Healthcare Data : First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings

Andreas K. Maier

ISBN 10: 3031476786 / ISBN 13: 9783031476785

Published by Springer Nature Switzerland, Springer International Publishing, 2023

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783031476785

Bibliographic Details

• Title: Machine Learning for Multimodal Healthcare Data : First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings
• Publisher: Springer Nature Switzerland, Springer International Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 3031476786
• ISBN 13: 9783031476785
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 312 grams

About this title

Synopsis

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.