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Can I read Deep Learning-based Natural Language Processing for Detecting Medical Symptoms and Histories in Emergency Patient Triage on EtoBox?
Deep Learning-based Natural Language Processing for Detecting Medical Symptoms and Histories in Emergency Patient Triage by Siryeol Lee; Juncheol Lee; Juntae Park; Jiwoo Park; Dohoon Kim; Joohyun Lee; Jaehoon Oh is a Medicine article available to read on EtoBox.
What is Deep Learning-based Natural Language Processing for Detecting Medical Symptoms and Histories in Emergency Patient Triage about?
Objective: The manual recording of electronic health records (EHRs) by clinicians in the emergency department (ED) is time-consuming and challenging. In light of recent advancements in large language models (LLMs) such as GPT and BERT, this study aimed to design and validate LLMs for automatic clinical diagnoses. The models were designed to identify 12 medical symptoms and 2 patient histories from simulated clinician-patient conversations within 6 primary symptom scenarios in emergency triage rooms. Materials and method: We developed classification models by fine-tuning BERT, a transformer-based pre-trained model. We subsequently analyzed these models using eXplainable artificial intelligence (XAI) and the Shapley additive explanation (SHAP) method. A Turing test was conducted to ascertain the reliability of the XAI results by comparing them to the outcomes of tasks performed and explained by medical workers. An emergency medicine specialist assessed the results of both XAI and the medical workers. Results: We fine-tuned four pre-trained LLMs and compared their classification performance. The KLUE-RoBERTa-based model demonstrated the highest performance (F1-score: 0.965, AUROC: 0.8
Who reads Deep Learning-based Natural Language Processing for Detecting Medical Symptoms and Histories in Emergency Patient Triage?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Siryeol Lee; Juncheol Lee; Juntae Park; Jiwoo Park; Dohoon Kim; Joohyun Lee; Jaehoon Oh
- Publisher
- Elsevier BV
- Published
- 2024
- Language
- EN
- Field
- Medicine (Health Sciences)