Harnessing Machine Learning to Understand SARS-CoV-2 Variants and Hospitalization Risk


In this session, we will delve deep into the transformative potential of Machine Learning (ML) in the BioTech and Pharma industry. This talk will provide a comprehensive overview of how ML can be harnessed to accelerate drug discovery, enhance personalized medicine, improve patient outcomes, and drive innovation. We will explore real-world applications, focusing on a case study that involves the analysis of SARS-CoV-2 genetic variants and their association with hospitalization risk. This will provide attendees with a practical understanding of how ML can be applied to complex biological and medical data to derive actionable insights. The session will provide a detailed walkthrough of the use of ML models like XGBoost and analytical techniques like SHapley Additive exPlanations (SHAP) analysis. In addition to exploring these tools and techniques, we will also discuss the challenges that come with integrating ML into existing bioinformatics workflows.


Tomasz Adamusiak MD Ph.D. is a Chief Scientist in the Clinical Insights & Innovation Cell at MITRE. He leads a multi-disciplinary group driving high-impact contributions to private and public sectors in Clinical and Genomic Data Science. Before MITRE, Tomasz was the Head of Data Science in the Pfizer Innovation Research (PfIRe) Lab. His team was responsible for developing novel digital endpoints, designing decentralized approaches for clinical trials, and applying AI/machine learning methods to generate novel insights from clinical data. Tomasz served in leadership and advisory roles in the American Medical Informatics Association, the SNOMED International, and the Epic Research Data Network.

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