用大模型和机器学习分析急诊精神科患者行为，辅助快速诊断。
Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?
- 结合行为数据与大模型分析，构建急诊精神疾病辅助诊断方法。
- 在德国救援站数据上，模型可有效识别异常心理状态的患者。
- 适合急救场景下医护人员快速筛查精神健康问题。
精神障碍是与压力及社会、职业或家庭功能受损相关的临床显著行为模式。由于缺乏明显体征，此类患者常被误判或漏诊。在紧急情况下，及时识别精神问题尤为关键且困难。本文研究传统机器学习与大语言模型（LLM）如何基于急诊精神科患者的行為模式进行诊断评估。数据来源于德国某救援站的急诊患者记录。实验采用包括Llama 3.1在内的多种机器学习模型，分析其在救援情境中预测精神障碍的能力，验证其作为高效辅助工具的可行性。
原文摘要 · Abstract (English)
Mental disorders are clinically significant patterns of behavior that are associated with stress and/or impairment in social, occupational, or family activities. People suffering from such disorders are often misjudged and poorly diagnosed due to a lack of visible symptoms compared to other health complications. During emergency situations, identifying psychiatric issues is that's why challenging but highly required to save patients. In this paper, we have conducted research on how traditional machine learning and large language models (LLM) can assess these psychiatric patients based on their behavioral patterns to provide a diagnostic assessment. Data from emergency psychiatric patients were collected from a rescue station in Germany. Various machine learning models, including Llama 3.1, were used with rescue patient data to assess if the predictive capabilities of the models can serve as an efficient tool for identifying patients with unhealthy mental disorders, especially in rescue cases.
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