用大型语言模型在精神病医院住院患者中表征研究领域标准症状
Thomas H McCoy1,2, Roy H Perlis1,2
1Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.
大型语言模型可以从精神病患者的笔记中估计研究领域标准 (RDoC) 维度,显示出临床应用的希望. 这些人工智能驱动的症状负担估计与现有方法相关,并预测住院时间和再接收风险.
科学领域:
- 精神病学是一个精神病学.
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 研究领域标准 (RDoC) 框架为精神病理学提供了一种跨诊断的方法.
- 从临床笔记中准确估计RDoC尺寸对于研究和临床实践至关重要.
- 大型语言模型 (LLM) 显示了处理复杂临床文本数据的潜力.
研究的目的:
- 评估LLMs在估计RDoC尺寸中的能力,从成年精神病医院住院患者的临床笔记.
- 评估LLM衍生的RDoC症状负担估计的融合和预测有效性.
- 探索使用LLM用于RDoC框架的现实应用的可行性.
主要方法:
- 来自电子健康记录的3619名成年精神病医院住院患者的回顾性队列研究 (2009-2015).
- 符合HIPAA的LLM (gpt-4-1106-预览版) 用于对RDoC维度的入学和放学笔记进行评分.
- 通过将LLM成绩与先前验证的方法相关联来评估收的有效性;根据住院时间和再入院概率检查预测有效性.
主要成果:
- 从LLM获得的RDoC得分与先前验证的评分方法 (肯德尔的tau从0.07到0.27) 显示了适度的相关性.
- 在认知,感觉运动,消极和社会领域的高分数预测了更长的住院时间;正值预测了更短的住院时间.
- 积极的价值,社会和兴奋领域预测了180天内再入院的风险.
结论:
- 来自LLM的RDoC精神病理学估计表明有希望的融合和预测有效性.
- 这种人工智能驱动的方法可能会提高在临床环境中应用RDoC框架的可行性.
- 在精神科护理中,LLM为客观的症状负担评估提供了一个潜在的工具.
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