Track / Overview

Advances in AI/ML are seen critical to advance our understanding of the disease and to bring better and more efficacious treatments to patients. As part of the drug development life-cycle vast amounts of clinical trials data are collected in order to identify targets of interest, discover biomarkers to stratify patients who could benefit from the drug, and to study the safety and benefit profile of the drug. Furthermore, after the drug is brought to the market its use in broader population is collected in a wide range of real-world data sources including, but not limited to, electronic medical records, disease registries, health insurance claims​, and digital devices​. Thus far the Pharma industry has not leveraged on the wealth of this information to deliver truly personalized care for patients.

Development of advanced ​statistical and machine learning​ methodologies combined with the availability of scalable computing environments is fueling a new wave of digitization in Pharma R&D pipelines thereby creating possibilities to discover and develop personalized medicines. This track would invite experts from industry and academia to share their experiences in using AI/ML for Pharma R&D. 

Track / Schedule

Data Science for Pharma R&D – Opportunities & Challenges

With Asif Jan

Developing Digital Measures from Person-Generated Health Data

With Luca Foschini

Detection and quantification of disease biomarkers in ophthalmology

With Agata Mosinska

Bayesian Neural Networks for toxicity prediction

With Elizaveta Semenova

A Machine Learning perspective on the emotional content of Parkinsonian speech

With Konstantinos Sechidis

On the Stability and Reproducibility of Data Science Pipelines

With Gavin Brown


Going beyond the average: causal machine learning for treatment effect heterogeneity estimation

With Karla DiazOrdaz

AE Brain – Detecting Adverse Drug Events with NLP

With Damir Bucar & Moritz Freidank

Defining and redefining human disease in electronic health records

With Spiros Denaxas

Fully unsupervised deep mode of action learning for phenotyping high-content cellular images

With Rens Janssens

Novartis benchmarking initiative: making sense of AI through the common task framework

With Mark Baillie

Artificial Intelligence in clinical development

With Marco Prunotto

Closing remarks

Track / Speakers

Elizaveta Semenova

Postdoc, AstraZeneca

Spiros Denaxas

Professor of Biomedical Informatics, University College London

Luca Foschini

Co-founder & Chief Data Scientist, Evidation Health

Gavin Brown

University of Manchester

Marco Prunotto

Head of Innovation, Late Stage Development, Roche

Asif Jan

Group Director, PHC Data Science, Roche

Rens Janssens

Research Software Engineer, Novartis

Moritz Freidank

Machine Learning Engineer, Novartis

Agata Mosinska

Research Scientist, RetinAI Medical

Konstantinos Sechidis

Postdoctoral Research Fellow, Roche

Mark Baillie

Director, Novartis

Karla DiazOrdaz

The Alan Turing Institute

Damir Bucar

Product Engineer, Novartis

Track / Co-organizers

Asif Jan

Group Director, PHC Data Science, Roche

Kurt Stockinger

Professor, ZHAW - Zurich University of Applied Sciences

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