Clinical Applications of Artificial Intelligence in Real-World Data

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.<p></p>