Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Health and Social Sciences | en_US |
| dc.contributor.advisor | Hui, Vivian (SN) | en_US |
| dc.creator | Lau, Ka Lok | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14208 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Artificial intelligence (AI) large language models (LLMS) for medical consultation and triage in the accident & emergency department (AED) | en_US |
| dcterms.abstract | Purpose | en_US |
| dcterms.abstract | In recent years, large language models (LLMs) in artificial intelligence (AI) have emerged as powerful tools for understanding human language. LLMs can classify patient triage results in the Accident & Emergency Department (AED). This study aims to estimate the accuracy of LLMs in AED triage results and assess the clinical relevance of these results. | en_US |
| dcterms.abstract | Methods | en_US |
| dcterms.abstract | The triage data in seven categories of chief complaints in AED from the open dataset, PhysioNet, were utilized in this study. A multi-query script was employed to query the LLMs using retrieval-augmented generation (RAG) based on the AED Emergency Severity Level (ESI). The models were compared, and qualitative methods were employed to collect feedback from domain experts. | en_US |
| dcterms.abstract | Results | en_US |
| dcterms.abstract | Approximately 500 validation cases in AED were analyzed, representing the first existing results from LLMs. The total average accuracy rate was 94.8% after adjustments based on domain experts" comments. The BLEU-4 rate, which checks the similarity between LLM results and original AED notes, was 39.15%. The ROUGE-Lsum score, which assesses the similarity of summarization between LLM explanations and the AED ESI triage guidelines, was 40.65%. | en_US |
| dcterms.abstract | Conclusions | en_US |
| dcterms.abstract | This study demonstrates that current LLMs can generate reliable information for low acuity levels (2-5), as verified by domain experts in the AED, potentially reducing misdiagnosis and improving outcomes. Domain experts found the explanations provided by LLMs regarding triage outcomes to be comprehensible. Importantly, the triage results generated by LLMs, including those with hallucination' content, were reviewed by domain experts, thereby reducing the incidence of incorrect or nonsensical outcomes. | en_US |
| dcterms.extent | vii, 56 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Artificial intelligence -- Medical applications | en_US |
| dcterms.LCSH | Hospitals -- Emergency services | en_US |
| dcterms.LCSH | Emergency medicine | en_US |
| dcterms.LCSH | Triage (Medicine) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8953.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.46 MB | Adobe PDF | View/Open |
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