Represents a thorough review for clinicians and researchers to review the potential of AI and big data in medicine
Includes extensive instructional material to structure thinking on the use of these techniques within clinical medicine
Provides practical exemplars throughout the book for readers to work on real-world data
Buy it now
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, access via your institution.
Table of contents (17 chapters)
Data Processing, Storage, Regulations
About this book
Clinical Applications of Artificial Intelligence in Real-World Data is a critical resource for anyone interested in the use and application of data science within medicine, whether that be researchers in medical data science or clinicians looking for insight into the use of these techniques.
- Big health data
- Artificial intelligence
- Machine learning
- Deep learning
- Biomedical ontologies
- Electronic Health Records
Editors and Affiliations
Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
Folkert W. Asselbergs
Institute of Health Informatics, University College London, London, UK
Department of Data Science and Biostatistics, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
Daniel L. Oberski
Cedars-Sinai Medical Center, Los Angeles, USA
Jason H. Moore
About the editors
Dr Spiros Denaxas is a Professor in Biomedical Informatics based at the Institute of Health Informatics at University College London and Associate Director leading phenomics at the British Heart Foundation Data Science Centre. His lab’s research focuses on creating and evaluating novel computational methods for data modelling, phenotyping, and disease subtype discovery in structured electronic health records.
Dr. Daniel L. Oberski is full professor of Health and Social Data Science with dual appointments at Utrecht University’s Department of Methodology & Statistics and the Department of Biostatistics and Data Science at the Julius Center, University Medical Center Utrecht (UMCU). His work focuses on applications of machine learning and data science to applied medical and social research, as well as the development of novel methods, often involving latent variable models. Among other roles, he is task coordinator of the Social Data Science team at the Dutch national infrastructure for the social sciences ODISSEI, and methodological lead at UMCU’s Digital Health team.
Dr. Jason Moore is founding Chair of the Department of Computational Medicine at Cedars-Sinai Medical Center where he also serves as founding Director of the Center for Artificial Intelligence Research and Education (CAIRE). He leads an active NIH-funded research program focused on the development and application of cutting-edge AI and machine learning algorithms for the analysis of biomedical data. His recent work has focused on methods for automated machine learning (AutoML) with a goal of democratizing AI in healthcare and biomedical research. He is an elected fellow of the American College of Medical Informatics, the International Academy of Health Sciences Informatics, the American Statistical Association, the International Statistics Institute, and the American Association for the Advancement of Science. He is Editor-in-Chief of the open-access journal BioData Mining.
Book Title: Clinical Applications of Artificial Intelligence in Real-World Data
Editors: Folkert W. Asselbergs, Spiros Denaxas, Daniel L. Oberski, Jason H. Moore
Publisher: Springer Cham
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-36677-2Published: 05 November 2023
Softcover ISBN: 978-3-031-36680-2Due: 19 November 2024
eBook ISBN: 978-3-031-36678-9Published: 04 November 2023
Edition Number: 1
Number of Pages: VI, 285
Number of Illustrations: 22 b/w illustrations, 58 illustrations in colour