Speakers: Catalina Vallejos, Scott Ritchie
29 Aprile 2026 | 10:00
DEIB, Sala "Schiavoni" (Ed. 20A)
Per maggiori informazioni: Marco Masseroli | marco.masseroli@polimi.it - Silvia Cascianelli | silvia.cascianelli@polimi.it
Sommario
As part of the Data Science Seminars – Bioinformatics Focus, two invited talks will take place on Wednesday, April 29th, 2026 at 10:00 am, at DEIB, in the “Schiavoni” Room (Building 20A - ground floor) organized by the Data Science for Bioinformatics group.Catalina Vallejos, Institute of Genetics and Cancer, College of Medicine & Veterinary Medicine, University of Edinburgh, will hold the seminar: "Using routine healthcare data to predict future health".
Scott Ritchie, Department of Public Health and Primary Care Unit, University of Cambridge, will hold the seminar: "Leveraging multi-omics to improve prediction and prevention of cardiometabolic diseases".
Abstracts for both talks are provided below.
"Using routine healthcare data to predict future health" - Catalina Vallejos
Can we identify who will experience an adverse health event (e.g. disease onset) weeks, months or even years before it happens? Questions like this are at the core of health data science research and have been empowered by the increasing ability to securely access routinely collected electronic health records (EHR). A key exemplar in Scotland is SPARRAv4 (Scottish Patients at Risk of Readmission and Admission version 4), a population-wide model that will be soon deployed to support anticipatory care planning. I will discuss some of the practical and methodological challenges that arise in the development and evaluation of such models, focusing on time-to-event outcomes. I will introduce the "C-index multiverse", highlighting how different conceptual and implementation choices can affect model comparison and hinder reproducibility. Finally, I will introduce landmaRk as a flexible tool to perform dynamic risk prediction in the presence of latent population heterogeneity.
"Leveraging multi-omics to improve prediction and prevention of cardiometabolic diseases" - Scott Ritchie
Efforts to reduce cardiometabolic diseases focus on controlling major modifiable risk factors through targeted intervention in people identified at high risk. However, our understanding of their aetiology remains incomplete, hindering our ability to both predict and prevent these diseases. To address this, we leverage multi-omics data in population cohorts and biobanks to identify new potentially modifiable molecular targets as well as to assess evidence for the potential for multi-omics to enhance existing clinical risk prediction tools.