Clinical Applications of Artificial Intelligence in Real-World Data, 1st ed. 2023
Langue : Anglais
Coordonnateurs : Asselbergs Folkert W., Denaxas Spiros, Oberski Daniel L., Moore Jason H.
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.
Part 1: Data Processing, Storage, Regulations.- Biomedical Big Data: Opportunities and Challenges.- Quality Control, Data Cleaning, Imputation.- Data Security And Privacy Issues.- Data Standards and Terminology.- Biomedical Ontologies.- Graph Databases as Future Of Data Storage.- Data Integration, Harmonization.- Natural Language Processing And Text Mining- Turning Unstructured Data Into Structured.- Part 2: Analytics.- Statistical Analysis Statistical Analysis - Causality, Mendelian Randomization.- Statistical Analysis – Meta-Analysis/Reproducibility.- Machine Learning – Basic Concepts.- Machine Learning – Basic Supervised Methods.- Machine Learning – Basic Unsupervised Methods.- Machine Learning – Evaluation.- Machine Learning – Representation Learning/Feature Selection/Engineering.- Machine Learning – Interpretation.- Deep Learning – Prediction.- Deep Learning – Autoencoders.- Artificial Intelligence.- Machine Learning In Practice – Clinical Decision Support, Risk Prediction, Diagnosis.- Machine Learning In Practice – Evaluation Clinical Value, Guidelines.- Challenges Of Machine Learning and AI.
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.
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
Date de parution : 11-2023
Ouvrage de 285 p.
17.8x25.4 cm
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