12 citations · 20 across the 11 of their papers we have counts for
11 papers
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…
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series
Rita Kuznetsova, Alizée Pace, Manuel Burger +2
Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in prob…
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…
Language Model Training Paradigms for Clinical Feature Embeddings
Yurong Hu, Manuel Burger, Gunnar Rätsch +1
In research areas with scarce data, representation learning plays a significant role. This work aims to enhance representation learning for clinical time series by deriving univers…
Multi-modal Graph Learning over UMLS Knowledge Graphs
Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) fo…
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…
Temporal Label Smoothing for Early Event Prediction
Hugo Yèche, Alizée Pace, Gunnar Rätsch +1
Models that can predict the occurrence of events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This…
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
Hugo Yèche, Rita Kuznetsova, Marc Zimmermann +4
The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for dev…
Variational learning across domains with triplet information
Rita Kuznetsova, Oleg Bakhteev, Alexandr Ogaltsov
The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet…