Ongoing master projects
These projects are currently in progress
Using Large Language Models with Multimodal Time Series Data for Critical Event Prediction in Intensive Care Units
Student
Sophia Ehlers
external page LinkedIn Sophia Ehlers
Supervisor
Lakmal Meegahapola
Summary
The master thesis explores the potential of Large Language Models (LLMs) to predict critical events in Intensive Care Units, including mortality, acute kidney injury, and sepsis. The research utilizes multimodal time series data from prominent public datasets (HiRID, MIMIC-IV, and eICU) to develop innovative prediction models. The methodology implements specialized techniques including prompt engineering tailored for structured clinical time series data, medical-specific LLM fine-tuning, and comprehensive cross-dataset validation to explore model performance across diverse clinical settings.
Using Large Language Models for Sepsis Prediction on in Intensive Care Units
Student
Jan Berner
external page LinkedIn Jan Berner
Supervisor
Lakmal Meegahapola
Summary
This master’s thesis explores the use of large language models (LLMs) for sepsis prediction in an ICU setting. By converting multimodal time-series data into a text-based format, we aim to enable more personalized and early detection of sepsis. We evaluate this novel approach by benchmarking it against traditional methods to assess whether LLMs can improve predictive accuracy.