通过使用大规模的语言模型,从当前疾病史中诊断精神疾病
Norio Otsuka1, Yuu Kawanishi1, Fumimaro Doi1
1Department of Psychiatry, Nara Medical University, Kashihara, Japan.
Psychiatry and clinical neurosciences
|August 1, 2023
概括
一个新的自然语言处理模型准确地从患者病史中协助精神病诊断,匹配精神病医生的表现. 这种人工智能工具在改善临床环境中的诊断准确性方面表现有前途.
科学领域:
- 人工智能在医学中的应用
- 精神病诊断 精神病诊断 精神病诊断
- 自然语言处理自然语言处理.
背景情况:
- 自然语言处理 (NLP) 模型提供了使用当前疾病史 (HPI) 的精神病诊断援助的潜力.
- 之前的研究受限于范围 (仅限于主要疾病),样本规模小,缺乏与专家诊断的比较.
- 需要一个涵盖所有精神疾病的综合诊断模型.
研究的目的:
- 开发和验证一个准确的NLP-based诊断模型用于使用HPI的精神疾病.
- 将模型的诊断准确性与不同经验水平的精神病医生进行比较.
主要方法:
- 从2,642份出院摘要 (2007-2021) 中提取了HPI和诊断.
- 使用ICD-10第V章代码进行分类诊断.
- 利用UTH-BERT,在电子医疗记录上进行预训,以预测HPI的诊断.
- 对比模型预测与半指定精神病医生和住院医生的诊断.
主要成果:
- 在NLP模型中,主要诊断的匹配率达到74.3%.
- 这超过了半指定精神病医生 (71.5%) 和住院医生 (69.4%) 的准确性.
- 在一小部分情况下,模型 (84.9%) 和居民 (83.3%) 的表现相似.
结论:
- 开发的NLP模型准确地预测了HPI的主要精神病诊断.
- 该模型展示了作为在临床实践中提供诊断建议的有价值工具的潜力.
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