3 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
Data-Driven Discovery of Feature Groups in Clinical Time Series
Fedor Sergeev, Manuel Burger, Polina Leshetkina +3
Clinical time series data are critical for patient monitoring and predictive modeling. These time series are typically multivariate and often comprise hundreds of heterogeneous fea…
Towards Foundation Models for Critical Care Time Series
Manuel Burger, Fedor Sergeev, Malte Londschien +10
Notable progress has been made in generalist medical large language models across various healthcare areas. However, large-scale modeling of in-hospital time series data - such as…
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
Fabian Baldenweg, Manuel Burger, Gunnar Rätsch +1
Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities…
Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions
Samyak Jain, Manuel Burger, Gunnar Rätsch +1
Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions. Recent deep learning…
On the Importance of Clinical Notes in Multi-modal Learning for EHR Data
Severin Husmann, Hugo Yèche, Gunnar Rätsch +1
Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community. Previous research has shown that joint…