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

TL;DR

This paper introduces a new multimodal dataset and benchmark for emergency care, demonstrating that incorporating physiological waveforms like ECG improves AI prediction models for diagnoses and patient deterioration.

Contribution

It provides a publicly available dataset and baseline models that integrate multimodal data, including raw waveforms, to enhance clinical decision support in emergency medicine.

Findings

Models achieved AUROC > 0.8 for 609 conditions

Deterioration prediction AUROC > 0.8 for 14 of 15 targets

Inclusion of waveform data improves predictive performance

Abstract

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.