D-BSSE eSymposium _1 June 2021
Predicting clinical phenotypes with machine learning
The ongoing digitalisation of clinical health information creates new opportunities for building early warning systems for medical complications and predictive systems for clinical outcomes. In particular, the rich information that intensive care units record about critically ill patients can be used to develop machine learning-based early warning systems, for instance, for organ failure or for risk of mortality. In this symposium, we will describe these opportunities for big data analysis in medicine and the challenges in creating these predictive systems.
Programme
_15:30 Karsten Borgwardt, D-BSSE Machine Learning and Computational Biology Lab. Talk: Introduction to clinical phenotype prediction with machine learning (30')
_16:15 Patrick Schwab, GlaxoSmithKline, Artificial Intelligence & Machine Learning. Talk: Real-time Prediction of COVID-19 related Mortality using Electronic Health Records (30')
_17:00 Michal Rosen-Zvi, IBM Research & The Hebrew University. Talk: From prediction to causal inference: identifying unknown benefits of existing drugs from retrospective patients data (30')
_17:45 end
Link to recording on YouTube
external pagehttps://youtu.be/wisaLEy6A6g