MDS-ED: Multimodal Decision Support in the Emergency Department -- a Benchmark Dataset for Diagnoses and Deterioration Prediction in Emergency Medicine

• URL: http://arxiv.org/abs/2407.17856v3
• Date: Tue, 24 Sep 2024 15:20:57 GMT
• Title: MDS-ED: Multimodal Decision Support in the Emergency Department -- a Benchmark Dataset for Diagnoses and Deterioration Prediction in Emergency Medicine
• Authors: Juan Miguel Lopez Alcaraz, Hjalmar Bouma, Nils Strodthoff,
• Abstract summary: We introduce a dataset based on MIMIC-IV, a benchmarking protocol, and initial results for evaluating multimodal decision support in the emergency department. We use diverse data modalities from the first 1.5 hours after patient arrival, including demographics, biometrics, vital signs, lab values, and electrocardiogram waveforms.
• Score: 0.9503773054285559
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: A clinically meaningful comparative assessment of medical decision support in emergency care is challenging due to a lack of appropriate datasets with multimodal input modalities and comprehensive prediction task. This hampers measurable progress in the field. Results: We introduce a dataset based on MIMIC-IV, a benchmarking protocol, and initial results for evaluating multimodal decision support in the emergency department (ED). We use diverse data modalities from the first 1.5 hours after patient arrival, including demographics, biometrics, vital signs, lab values, and electrocardiogram waveforms. We analyze 1443 clinical labels across two contexts: predicting diagnoses and patient deterioration. Our diagnostic model achieves an AUROC score over 0.8 in a statistically significant manner for 609 out of 1428 conditions, including cardiac conditions like myocardial infarction and non-cardiac conditions such as renal disease and diabetes. The deterioration model scores above 0.8 in a statistically significant manner for 14 out of 15 targets, including critical events like cardiac arrest, mechanical ventilation, intensive care unit admission, as well as short- and long-term mortality. Furthermore, we provide one of the first robust demonstrations of the significant impact of raw waveform input data on model performance. Conclusions: This study highlights the proposed dataset as a unique resource to foster progress towards measurable progress in the domain of algorithmic decision support in emergency care. The presented multimodal baseline models showcase the potential of diagnostic decision support in the field and provide strong incentives for including raw waveform data.

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