text: 151,4,1466813039139864577,1159548392839753729,Hugo Yeche,"Happy to share ""HiRID-ICU-Benchmark"" (HiB), a new benchmark for patient monitoring tasks in the ICU. From the HiRID database, we define 6 diverse and clinically relevant tasks for +40K patients and +15M time-points. paper: <LINK> code: <LINK> <LINK> We provide an easy-to-use pipeline for data processing and labels extraction. This shared and reproducible pipeline will improve the comparison of future works. <LINK> This benchmark compares methods on a diverse set of clinically relevant tasks as summarized below. <LINK> Finally, we compared recent sequence DL architectures with conventional ML algorithms. In line with the original HiRID paper, we observe that lightGBM with hand-extracted features outperforms all DL methods. <LINK> This work is the result of a great collaboration with Rita Kuznetsova, Marc Zimmermann, @mhueser_, @xinruilyu under Martin Faltys, and @gxr supervision.  Feel free to reach out during NeurIPS Datasets and Benchmarks poster session 4",https://arxiv.org/abs/2111.08536,"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. ","HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on