Clinical Applications of Artificial Intelligence in Real-World Data, Kartoniert / Broschiert

Klappentext

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.