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.

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Evidence Matters is a virtual one-day summit that brings the literature review community together from different industries to learn, engage, and solve ever-pressing evidence-based research challenges.