人工智能和叙事嵌入通过出生故事检测产后创伤后应激障碍
Alon Bartal1, Kathleen M Jagodnik1,2,3, Sabrina J Chan2
1The School of Business Administration, Bar-Ilan University, Ramat Gan, 5290002, Israel.
Scientific reports
|April 11, 2024
概括
文本嵌入式ADA-002 (ADA) 模型有效地选母亲的分娩创伤后应激障碍 (CB-PTSD). 这种机器学习方法在诊断母亲精神疾病方面表现有前途.
科学领域:
- 精神病学是一个精神病学.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 基于机器学习 (ML) 的自然语言处理 (NLP) 显示出诊断精神疾病的潜力.
- 聊天生成预训练变压器 (ChatGPT) 已经证明了初步的可行性,但其在评估精神疾病方面的准确性需要进一步评估.
- 产后创伤性应激障碍 (CB-PTSD) 是一种严重的母亲精神疾病,缺乏标准的查方案.
研究的目的:
- 评估ChatGPT和文本嵌入ADA-002 (ADA) 模型在检测CB-PTSD方面的有效性.
- 通过分析母亲分娩叙述,探索这些人工智能模型在查CB-PTSD方面的潜力.
- 开发和验证一个ML模型来识别CB-PTSD使用数值向量表示.
主要方法:
- 招募了1295名在过去六个月内分娩的女性的样本.
- 使用ChatGPT和ADA模型分析了母亲的分娩叙述.
- 使用ADA模型的数值矢量表示来进行叙事分类,开发了一个ML模型.
- 作为CB-PTSD的评估工具,使用了DSM-5的PTSD检查清单 (PCL-5;截止点31).
主要成果:
- 使用ADA模型的ML模型在识别CB-PTSD时获得了F1得分0.81.
- 这一表现超过了ChatGPT和其他六个大型文本嵌入模型.
- 与ChatGPT相比,ADA模型在检测CB-PTSD方面表现出更好的能力.
结论:
- 当ADA模型被整合到ML框架中时,可以有效地利用它来识别CB-PTSD.
- 这种方法显示出作为对母亲精神疾病的查工具的重大前景.
- 开发的建模方法有可能对评估其他心理健康障碍进行概括.
关键词:
出生故事的故事.出生创伤的出生创伤.聊天GPT 聊天 在GPT 聊天与分娩有关的创伤后应激障碍 (CB-PTSD)母亲心理健康 母亲心理健康自然语言处理 (NLP)在产后创伤后应激障碍.预训练的大型语言模型 (PLM)更多相关视频
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