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

arXiv: https://arxiv.org/abs/2111.08536 · HTML: https://mlsanity.com/paper/2111.08536/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Efficient Content-Based Sparse Attention with Routing Transformers](https://mlsanity.com/paper/2003.05997/) (2020)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [Set Functions for Time Series](https://mlsanity.com/paper/1909.12064/) (2019)
- [Neighborhood Contrastive Learning Applied to Online Patient Monitoring](https://mlsanity.com/paper/2106.05142/) (2021)
- [Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data](https://mlsanity.com/paper/2010.01149/) (2020)
- [Early hospital mortality prediction using vital signals](https://mlsanity.com/paper/1803.06589/) (2018)

## Built on by (most cited first, 30 of 39)

- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization](https://mlsanity.com/paper/2211.02733/) (2022)
- [Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine](https://mlsanity.com/paper/2311.04937/) (2023)
- [ER-Reason: A Benchmark Dataset for LLM Clinical Reasoning in the Emergency Room](https://mlsanity.com/paper/2505.22919/) (2025)
- [EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis](https://mlsanity.com/paper/2510.25628/) (2025)
- [Improving Neural Additive Models with Bayesian Principles](https://mlsanity.com/paper/2305.16905/) (2023)
- [Temporal Label Smoothing for Early Event Prediction](https://mlsanity.com/paper/2208.13764/) (2022)
- [A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care](https://mlsanity.com/paper/2209.07805/) (2022)
- [On the Importance of Clinical Notes in Multi-modal Learning for EHR Data](https://mlsanity.com/paper/2212.03044/) (2022)
- [A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data](https://mlsanity.com/paper/2411.12274/) (2024)
- [Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding](https://mlsanity.com/paper/2306.01157/) (2023)
- [Continual learning of longitudinal health records](https://mlsanity.com/paper/2112.11944/) (2021)
- [On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series](https://mlsanity.com/paper/2311.08902/) (2023)
- [Dynamic Survival Analysis for Early Event Prediction](https://mlsanity.com/paper/2403.12818/) (2024)
- [LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records](https://mlsanity.com/paper/2502.14259/) (2025)
- [Towards Foundation Models for Critical Care Time Series](https://mlsanity.com/paper/2411.16346/) (2024)
- [Can we generate portable representations for clinical time series data using LLMs?](https://mlsanity.com/paper/2603.23987/) (2026)
- [Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions](https://mlsanity.com/paper/2311.07180/) (2023)
- [Intensive Care as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2402.17501/) (2024)
- [From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU](https://mlsanity.com/paper/2303.15354/) (2023)
- [Multi-modal Graph Learning over UMLS Knowledge Graphs](https://mlsanity.com/paper/2307.04461/) (2023)
- [Large Language Model as a Universal Clinical Multi-task Decoder](https://mlsanity.com/paper/2406.12738/) (2024)
- [DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation](https://mlsanity.com/paper/2503.22257/) (2025)
- [Domain Generalization and Adaptation in Intensive Care with Anchor Regression](https://mlsanity.com/paper/2507.21783/) (2025)
- [SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis](https://mlsanity.com/paper/2511.11935/) (2025)
- [TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning](https://mlsanity.com/paper/2602.14200/) (2026)
- [SepAl: Sepsis Alerts On Low Power Wearables With Digital Biomarkers and On-Device Tiny Machine Learning](https://mlsanity.com/paper/2408.08316/) (2024)
- [Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions](https://mlsanity.com/paper/2506.19035/) (2025)

## Abstract (arXiv)

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.