用临床实践指南对抑郁症进行诊断解释的交叉注意力方法
IEEE journal of biomedical and health informatics
|October 17, 2024
概括
我们开发了ProcesS知识注入交叉注意力 (PSAT) 来解释使用临床知识的抑郁症数据的人工智能分类. 这增强了对心理健康查工具的信任和准确性.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 心理健康研究 心理健康研究
背景情况:
- 可解释性是人工智能在分析临床对话中的采用障碍.
- 在线心理健康社区为AI分析提供了大量尚未开发的数据.
- 临床医生的专业知识可以引导人工智能了解患者的互动.
研究的目的:
- 开发一种人工智能方法,利用临床知识来分类和解释与抑郁相关的数据.
- 增强变压器模型,为心理健康从业者 (MHPs) 提供可理解的解释.
- 提高AI在心理健康查中的可信度和效率.
主要方法:
- 建议的过程S知识注入交叉注意力 (PSAT),一个包含临床实践指南 (CPG) 的域一般架构.
- 通过使用SNOMED-CT.CT,将专注于抑郁症的CPG (例如PHQ-9) 转化为机器可读的本体学.
- 通过整合临床知识,增强了GPT-3.5通过整合临床知识来产生应用相关解释的能力.
主要成果:
- 在四个专家策划的数据集中,PSAT为与抑郁症相关的数据生成了与应用相关的解释.
- 在分类和解释生成方面,PSAT的表现优于12个基线模型.
- 在其他模型失败的情况下,PSAT提供了解释.
结论:
- PSAT有效地将临床知识集成到AI模型中,用于解释性抑郁症分析.
- 该PSAT架构增强了对人工智能工具用于心理健康查的信任和实用性.
- 这种方法对在线和离线的心理健康应用都有很大的潜力.
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