Search Results for author: Hugo Yèche

Found 8 papers, 4 papers with code

Dynamic Survival Analysis for Early Event Prediction

no code implementations19 Mar 2024 Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Rätsch

This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics.

Management Survival Analysis

On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series

no code implementations15 Nov 2023 Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch

Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features.

Time Series

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding

no code implementations1 Jun 2023 Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz

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 observed outcomes.

Management Offline RL +2

Improving Neural Additive Models with Bayesian Principles

1 code implementation26 May 2023 Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin

Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks.

Additive models Bayesian Inference +1

On the Importance of Clinical Notes in Multi-modal Learning for EHR Data

no code implementations6 Dec 2022 Severin Husmann, Hugo Yèche, Gunnar Rätsch, Rita Kuznetsova

Understanding deep learning model behavior is critical to accepting machine learning-based decision support systems in the medical community.


Temporal Label Smoothing for Early Event Prediction

1 code implementation29 Aug 2022 Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova

TLS reduces the number of missed events by up to a factor of two over previously used approaches in early event prediction.

Binary Classification Circulatory Failure +3

HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data

1 code implementation NeurIPS Datasets and Benchmarks 2021 Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch

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 developing and comparing such methods.

BIG-bench Machine Learning Circulatory Failure +7

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

1 code implementation9 Jun 2021 Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch

Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients.

BIG-bench Machine Learning Contrastive Learning +3

Cannot find the paper you are looking for? You can Submit a new open access paper.