Transparent Machine Learning
Machine learning (ML) aims to discover patterns from data that can be used for prediction. The use of “black-box” ML models in healthcare research and decision-making has been limited due to clinical liability and lack of trust from stakeholders. FDA guidelines for ML-based devices mandate transparency to assure continual safety and efficiency as notable recent failures have prompted increasing ML research into bias, fairness and causality.
In this webinar, Alind presents our continuing work in immuno-oncology using Bayesian network models for predicting safety and survival outcomes, extrapolating from limited follow-up data and validating with external real-world data for key subgroups. He also presents ways to incorporate subject-matter expertise and causality, and address ways to enhance transparency and communication for stakeholders.
Meet the Speaker
Alind Gupta, Machine Learning Specialist, Cytel
Alind Gupta is a Machine learning specialist at Cytel in Toronto, Canada focusing on probabilistic graphical models and Bayesian inference. His current work focuses on the use of Bayesian networks and Markov models for modelling heterogeneity in response to cancer immunotherapy and for long-term survival prediction using clinical trial and real-world data. Alind has a PhD from the University of Toronto studying genetics of rare diseases.