"Time you enjoy wasting is not wasted time" - Marthe Troly-Curtin
- hyeche@ inf.ethz.ch
Department of Computer Science
Biomedical Informatics Group
- CAB F53.1
I am interested in solving problems arising from deep learning approaches in health care. With this purpose, I focus on designing techniques leveraging prior knowledge and unlabeled data to bring current methods closer to clinicians.
After two years of preparatory classes I entered Telecom Paris as my engineering school for the following three years. During my first year, I obtained my B.Sc in the field of “Science of Engineering”. I then moved to Sophia-Antipolis for my second year to join EURECOM as a double degree with Telecom Paris. Finally I completed my last year of master at ENS Paris Saclay, joining the “Mathematiques, Vision, Apprentissage” (MVA) master’s program where I studied applied mathematics and computer vision in machine learning. During my last 6 months of master, I completed a research internship within Imagia, a Montreal-based start-up. There I worked on interpretable deep learning in the context of medical Imaging. I joined the Biomedical Informatics group in 2020 for my Ph.D.
Abstract Models that can predict adverse events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging machine learning task remains typically treated as simple binary classification, with few bespoke methods proposed to leverage temporal dependency across samples. We propose Temporal Label Smoothing (TLS), a novel learning strategy that modulates smoothing strength as a function of proximity to the event of interest. This regularization technique reduces model confidence at the class boundary, where the signal is often noisy or uninformative, thus allowing training to focus on clinically informative data points away from this boundary region. From a theoretical perspective, we also show that our method can be framed as an extension of multi-horizon prediction, a learning heuristic proposed in other early prediction work. TLS empirically matches or outperforms considered competing methods on various early prediction benchmark tasks. In particular, our approach significantly improves performance on clinically-relevant metrics such as event recall at low false-alarm rates.
Authors Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
Abstract 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. While raw datasets, such as MIMIC-IV or eICU, can be freely accessed on Physionet, the choice of tasks and pre-processing is often chosen ad-hoc for each publication, limiting comparability across publications. In this work, we aim to improve this situation by providing a benchmark covering a large spectrum of ICU-related tasks. Using the HiRID dataset, we define multiple clinically relevant tasks in collaboration with clinicians. In addition, we provide a reproducible end-to-end pipeline to construct both data and labels. Finally, we provide an in-depth analysis of current state-of-the-art sequence modeling methods, highlighting some limitations of deep learning approaches for this type of data. With this benchmark, we hope to give the research community the possibility of a fair comparison of their work.
Authors Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch
Submitted NeurIPS 2021 (Datasets and Benchmarks)
Abstract Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with a novel contrastive learning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series.
Authors Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
Submitted ICML 2021