18 citations · 37 across the 12 of their papers we have counts for
13 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…
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk +2
Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace…
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace, Hugo Yèche, Bernhard Schölkopf +2
A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the obse…
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…
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch +2
Data augmentation is commonly applied to improve performance of deep learning by enforcing the knowledge that certain transformations on the input preserve the output. Currently, t…
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Hugo Yèche, Gideon Dresdner, Francesco Locatello +2
Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monito…
Boosting Variational Inference With Locally Adaptive Step-Sizes
Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa +2
Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Vari…