Clinical Applications of Artificial Intelligence in Real-World Data
Folkert W. Asselbergs · Spiros Denaxas · Daniel L. Oberski · Jason H. Moore
nov. 2023 · Springer Nature
Carte electronică
285
Pagini
reportEvaluările și recenziile nu sunt verificate Află mai multe
Despre această carte electronică
This book is a thorough and comprehensive guide to the use of modern data science within health care. Critical to this is the use of big data and its analytical potential to obtain clinical insight into issues that would otherwise have been missed and is central to the application of artificial intelligence. It therefore has numerous uses from diagnosis to treatment.
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.
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.
Despre autor
Dr Folkert Asselbergs is a clinical cardiologist at Amsterdam Heart Center, Prof of Precision medicine at the Institute of Health Informatics, University College London, director and founder of the BRC Clinical Research Informatics Unit and the recently initiated Nudging Unit at University College London Hospital, chair of the data infrastructure of the Dutch Cardiovascular Alliance, and associate editor of European Heart Journal for digital health and innovation. His research program focuses on translational data science using existing health data such as electronic health records and clinical registries enriched with novel modalities such as -omics and sensor data for knowledge discovery, drug target validation and precision medicine in cardiovascular disease.
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.
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.
Evaluează cartea electronică
Spune-ne ce crezi.
Informații despre lectură
Smartphone-uri și tablete
Instalează aplicația Cărți Google Play pentru Android și iPad/iPhone. Se sincronizează automat cu contul tău și poți să citești online sau offline de oriunde te afli.
Laptopuri și computere
Poți să asculți cărțile audio achiziționate pe Google Play folosind browserul web al computerului.
Dispozitive eReader și alte dispozitive
Ca să citești pe dispozitive pentru citit cărți electronice, cum ar fi eReaderul Kobo, trebuie să descarci un fișier și să îl transferi pe dispozitiv. Urmează instrucțiunile detaliate din Centrul de ajutor pentru a transfera fișiere pe dispozitivele eReader compatibile.
