Clinical applications of artificial intelligence in real-world data / Folkert W. Asselbergs, Spiros Denaxas, Daniel L. Oberski, Jason H. Moore, editors.
Asselbergs, Folkert. W., 1976-; Denaxas, Spiros, editor.; Oberski, Daniel L. editor.; Moore, Jason H., editor.
2023
R859.7.A78
Available Online

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Title Clinical applications of artificial intelligence in real-world data / Folkert W. Asselbergs, Spiros Denaxas, Daniel L. Oberski, Jason H. Moore, editors.
ISBN 9783031366789 (electronic bk.)
3031366786 (electronic bk.)
3031366778
9783031366772
DOI https://doi.org/10.1007/978-3-031-36678-9
Published Cham : Springer, 2023.
Language English
Description 1 online resource (376 pages) : illustrations (black and white, and color).
Item Number 10.1007/978-3-031-36678-9 doi
Call Number R859.7.A78
Dewey Decimal Classification 610.285/63
Summary 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.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Asselbergs, Folkert. W., 1976-
Denaxas, Spiros, editor.
Oberski, Daniel L. editor.
Moore, Jason H., editor.
Available in Other Form CLINICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN REAL-WORLD DATA.
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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.

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