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.

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.