Rule-Based Expert Systems

This lecture was developed as part of a graduate-level course on 'artificial intelligence for medical applications and biomedical research' within the Cedars Sinai Health University. This course will explore how AI can be a driving force for automated clinical decision support and medical discovery. We will explore concepts in logic, knowledge representation, expert systems for automated decision-making, search algorithms, uncertainty in reasoning, and other related topics that will enable you to develop, understand, and apply health AI solutions effectively and ethically. We will explore how AI encompasses and differs from machine learning and the distinction between inductive and deductive reasoning. In a practical sense, the course will provide you with the tools to organize, represent, interpret, and search biomedical data to derive knowledge, automate decisions, and make predictions while avoiding bias. Chapters: 0:00 Intro 0:28 Introducing Rule-Based Expert Systems 4:03 Knowledge Base 9:23 Inference Engine 19:28 Forward Chaining 22:51 Backward Chaining 27:27 Inference Engine Efficiency 28:47 Other Components 31:30 Summary