# Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care

arXiv: https://arxiv.org/abs/2407.17856 · HTML: https://mlsanity.com/paper/2407.17856/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [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)
- [Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery](https://mlsanity.com/paper/2305.17043/) (2023)
- [Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata](https://mlsanity.com/paper/2308.15291/) (2023)
- [ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance](https://mlsanity.com/paper/2402.13448/) (2024)
- [S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models](https://mlsanity.com/paper/2310.06715/) (2023)
- [Explaining Time Series Classification Predictions via Causal Attributions](https://mlsanity.com/paper/2405.15871/) (2024)
- [From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence](https://mlsanity.com/paper/2207.12958/) (2022)

## Built on by (most cited first, 15 of 15)

- [Benchmarking ECG FMs: A Reality Check Across Clinical Tasks](https://mlsanity.com/paper/2509.25095/) (2025)
- [CardioLab: Laboratory Values Estimation from Electrocardiogram Features - An Exploratory Study](https://mlsanity.com/paper/2407.18629/) (2024)
- [Abnormality Prediction and Forecasting of Laboratory Values from Electrocardiogram Signals Using Multimodal Deep Learning](https://mlsanity.com/paper/2411.14886/) (2024)
- [Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study](https://mlsanity.com/paper/2412.07737/) (2024)
- [A Multimodal Data Processing Pipeline for MIMIC-IV Dataset](https://mlsanity.com/paper/2601.11606/) (2026)
- [Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach](https://mlsanity.com/paper/2412.03717/) (2024)
- [A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking](https://mlsanity.com/paper/2601.06645/) (2026)
- [SignalMC-MED: A Multimodal Benchmark for Evaluating Biosignal Foundation Models on Single-Lead ECG and PPG](https://mlsanity.com/paper/2603.09940/) (2026)
- [ECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction](https://mlsanity.com/paper/2609.21755/) (2026)
- [Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms](https://mlsanity.com/paper/2502.04918/) (2025)
- [Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning](https://mlsanity.com/paper/2509.17674/) (2025)
- [Transferring Clinical Knowledge into ECGs Representation](https://mlsanity.com/paper/2512.07021/) (2025)
- [EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses](https://mlsanity.com/paper/2607.28788/) (2026)
- [Hidden Data Challenges in Clinical Machine Learning](https://mlsanity.com/paper/2610.13350/) (2026)
- [Retrieval-Augmented Multimodal Language Model with Heterogeneous Evidence Integration for Clinical Prognosis](https://mlsanity.com/paper/2610.13510/) (2026)

## Abstract (arXiv)

Background: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including raw waveform signals, remains underexplored in clinical decision support. Methods: We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care. Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration. Results: The diagnostic model achieves area under the receiver operating curve (AUROC) scores above 0.8 for 609 out of 1,428 conditions, covering both cardiac (e.g., myocardial infarction) and non-cardiac (e.g., renal disease, diabetes) diagnoses. The deterioration model attains AUROC scores above 0.8 for 14 out of 15 targets, accurately predicting critical events such as cardiac arrest, mechanical ventilation, ICU admission, and mortality. Conclusions: Our study highlights the positive impact of incorporating raw waveform data into decision support models, improving predictive performance. By introducing a unique, publicly available dataset and baseline models, we provide a foundation for measurable progress in AI-driven decision support for emergency care.