Title: Artificial Intelligence for Drug Development, Precision Medicine and Healthcare
Instructors: Professor Mark Chang, Boston University
Moderator: Ivan S. F. Chan
Artificial intelligence (AI) or machine learning (ML) has been used in drug discovery in biopharmaceutical companies for nearly 20 years. More recently AI has also been used for the disease diagnosis and prognosis in healthcare. In analysis of clinical trial data, predicted individual patient outcomes for precision medicine, similarity based machine learning (SBML) has recently been proposed for clinical trials for oncology and rare disease without the requirement of big data.
The course will focus on supervised learning, including similarity-based learning and deep learning neural networks. We will also introduce unsupervised, reinforcement, and evolutionary learning methods. The short course aims at conceptual clarity and mathematical simplicity.
Provide R code for implementation with examples.
The course materials are based on instructor’s book (May, 2020): Artificial Intelligence in Drug Development, Precision Medicine, and Healthcare.
Day 1 Course will cover:
- A Brief AI History
- Classic Statistics versus AI
- Artificial General Intelligence Toward Wellbeing
- Similarity-Based Machine Learning
- Deep Learning: Convolutional Neural Net (CNN)
- Deep Learning: Recurrent Neural Net (RNN)
- Deep Learning: Deep Belief Net (DBN)
- Generative Adversarial Networks & Autoencoders
Day 2 Course will cover:
- Kernel Method & Support Vector Machine
- Decision Tree Methods
- Unsupervised Learning & Applications in Drug Development
- Reinforcement Learning & Applications in Clinical Development Program
- Collective Intelligence and Applications in Pharmaceutical R &D
- Evolutionary Learning & Applications in Drug Discovery
- Medical AI: Future Perspectives
Goals: attendees will learn common AI methods in drug development and medicine, be able to use the AI methods with R for clinical trial and other data, and be able to interpret the results.
Dr. Mark Chang is Sr. Vice President, Strategic Statistical Consulting at Veristat. Before joining Veristat, Chang was Vice President of Biometrics at AMAG Pharmaceuticals. Chang is a fellow of the American Statistical Association and an adjunct professor of Biostatistics at Boston University. He is a co-founder of the International Society for Biopharmaceutical Statistics, co- chair of the Biotechnology Industry Organization (BIO) Adaptive Design Working Group, and a member of the Multiregional Clinical Trial (MRCT) Expert Group. Chang has served associate editor for Journal of Pharmaceutical Statistics. He has been invited to serve as a co-chair on the scientific advisory board and organization committees for several national and international professional/academic conferences on statistics and clinical trial
designs. He has given over 50 lectures, short courses, and invited speeches at national and international conferences. Dr. Chang is an expert in adaptive clinical trials and other innovative approach in drug development and has been invited to present adaptive design and biomarker topics at the US Food and Drug Administration. Chang has broad research interests. He has published 10 books, including Adaptive Design Theory and Implementation Using SAS and R, Paradoxes in Scientific Inferences, Modern Issues and Methods in Biostatistics, Monte Carlo Simulation for the Pharmaceutical Industry, Principles of Scientific Methods, and Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials.