Summary of Ed-copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance, by Liwen Sun et al.
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
by Liwen Sun, Abhineet Agarwal, Aaron Kornblith, Bin Yu, Chenyan Xiong
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As a machine learning educator, I’ll summarize this abstract for a technical audience. This research proposes AI-based diagnostic assistance to reduce wait times and improve accuracy in emergency department (ED) triage. The team curated MIMIC-ED-Assist, a benchmark dataset using public patient data, to test AI systems suggesting laboratory tests that minimize wait time while predicting critical outcomes like death. They developed ED-Copilot, which uses a pre-trained bio-medical language model and reinforcement learning to sequentially suggest patient-specific laboratory tests and make diagnostic predictions. On MIMIC-ED-Assist, ED-Copilot outperforms baselines, halving average wait time from four hours to two hours, while achieving competitive performance without restrictions as maximum allowed time increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious high school students or non-technical adults, this paper is about using artificial intelligence (AI) to help doctors diagnose patients faster and more accurately. Right now, it takes a long time for doctors to gather all the information they need before making a diagnosis, which can be bad for patients. The researchers created a special dataset and AI system that suggests laboratory tests to doctors based on patient information, trying to balance speed with accuracy. Their system, called ED-Copilot, was tested on real data and worked well, getting the right diagnosis faster than usual. This could make a big difference in hospitals. |
Keywords
* Artificial intelligence * Language model * Machine learning * Reinforcement learning
