TITLE: Artificial Intelligence for Drug Development, Precision Medicine and Healthcare
INSTRUCTOR: Professor Mark Chang, Boston University
MODERATOR: Wenjin Wang



Artificial intelligence (AI) or machine learning (ML) has been used in drug discovery for many years under name of bioinformatics, such as sequencing, annotating genomes, analysis of gene and protein expression and regulation, linking the biological and disease network to the symptoms and adverse events, identifying structure-activity relationships in discovery and designing new drugable molecules. AI has also been used for the prediction of cancer susceptibility (risk assessment), cancer recurrence/local control, and cancer survival. In analysis of clinical trial data, predicted individual patient outcomes for precision medicine, similarity-based machine learning (SBML) has recently been used in clinical trials for oncology and rare disease without the requirement of big data as most ML methods do. The introductory course will cover supervised, unsupervised, semi-supervised, and reinforcement learning methods, and swarm and evolutionary intelligences. It aims at conceptual clarity and mathematical simplicity. Provide R code for some of the supervised learning methods and discussion case studies.

Table of Contents:

  • Overview AI methods in Medicine and Health
  • Supervised Learning Method

Tree-Based Methods

Support Vector Machine

Artificial neural network for Deep Learning


Convolutional Neural Networks

Recursive Neural Networks

Long Short-Term Memory Networks

Deep Belief Network

  • Similarity Based Method:

Nearest-Neighbors Method

Kernel Method

Similarity-based machine learning

Implementation in R

Clinical Trial Examples

(4) Other AI methods and Future Perspectives

Unsupervised Learning

Reinforcement Learning,

Evolutionary Intelligence

Future perspectives

Goals: attendees will learn common AI methods in drug development and medicine, be able to use the AI methods with R for medicine, especially for clinical trials, and be able to interpret the results.

Instructors’ Biography:

Dr. Mark Chang is founder of AGInception, a research organization for artificial general intelligence. He was Sr. Vice President, Strategic Statistical Consulting at Veristat and 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. Dr. Chang has published 10 books, including Adaptive Design Theory and Implementation Using SAS and RParadoxes in Scientific InferencesModern Issues and Methods in BiostatisticsMonte Carlo Simulation for the Pharmaceutical IndustryPrinciples of Scientific Methods, and Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials.


Ms. Susan Hwang is a PhD candidate, working on her thesis research on Artificial Intelligence for Clinical Trials in the Biostatistics Department at Boston University. She previously worked at Alkermes, a biopharmaceutical company, as biostatistician for 3 years. In addition, Susan worked as a data manager at Harvard School of Public Health and a research extern at Vertex Pharmaceuticals. Her academic interest is AI in drug development, and her other passion is training, fostering, and finding forever home for rescue dogs.

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