AI4HealthUOL/MDS-ED

Repository for the paper 'Enhancing Clinical Decision Support with Physiological Waveforms — A Multimodal Benchmark in Emergency Care'.

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Emerging

This project provides a comprehensive dataset and framework to help emergency department clinicians predict patient outcomes more accurately. It takes diverse patient data, including demographics, vital signs, lab results, and raw ECG waveforms, to predict discharge diagnoses and potential patient deterioration events. The primary users are medical researchers and healthcare professionals working on advanced clinical decision support systems.

No commits in the last 6 months.

Use this if you are a medical researcher or clinician needing a robust, multimodal dataset and benchmark to develop or evaluate AI models for predicting patient diagnoses and deterioration in emergency care.

Not ideal if you are looking for a plug-and-play clinical tool for immediate patient care rather than a research dataset and benchmarking framework.

emergency-medicine clinical-decision-support patient-diagnostics patient-deterioration biomedical-data
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 11 / 25

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Stars

22

Forks

3

Language

Python

License

MIT

Last pushed

Apr 30, 2025

Commits (30d)

0

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