Analytics and Informatics for Child Health

Research

Prof Ece  Özkan Elsen’s research focuses on computational models that integrate medical images, clinical data and additional modalities such as physiological signals to support clinicians in the diagnosis and management of paediatric diseases. Specifically, she aims to tackle the main challenges affecting the performance of machine learning models, including data scarcity, variability across datasets from different sources and algorithmic bias. By integrating multi-modal datasets and drawing inspiration from human behaviour, she will develop interpretable artificial intelligence-driven healthcare solutions that will contribute to advancing precision medicine for children and adolescents.

Prof Ece Özken Elsen

Prof Ece Özkan Elsen is a computational engineer and data scientist based in the Department of Biomedical Engineering at the University of Basel, where she is developing machine learning methods that are easy to interpret, fair, and generalizable for paediatric care. In 2018, she obtained her PhD in Electrical Engineering from ETH Zurich, where she continued her research activities as postdoctoral fellow before moving to the Massachusetts Institute of Technology in the USA. In addition to her academic experience, Prof Özkan Elsen has also worked as a data and analytics consultant for companies in the private sector.

Postdoctoral Researchers

  • Paul Fischer

PhD Students

  • Simon Böhi
  • Max Krähenmann

Open Positions

Publications

2025

2024

2023

2022

Analytics and Informatics for Child Health Projects

NEO-SEPSIS: Early Detection of Neonatal Sepsis Using Clinical and Physiological Data

MULTIMODAL-HEALTH: Generalizable AI Models for Pediatric and Adult Clinical Applications

SAFE-PEDS: Uncertainty-Aware AI for Safe Deployment in Pediatric Medicine

Prof Ece Özken Elsen develops computational models to support clinicians in the diagnosis and management of paediatric diseases. By integrating multi-modal datasets and drawing inspiration from human behaviour, she develops interpretable artificial intelligence-driven healthcare solutions that will contribute to advancing precision medicine for children and adolescents.

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