探索自然语言处理的使用,以客观地评估精神分裂症患者失调的言语
Lydia Jeong1, Melissa Lee2, Ben Eyre3,4
1Faculty of Information University of Toronto Toronto Ontario Canada.
Psychiatric research and clinical practice
|September 15, 2023
概括
自然语言处理 (NLP) 可以使用语音标记客观地测量精神分裂症症状严重程度. 这种方法有望改善临床环境中的急性精神病监测.
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:
- 在精神分裂症中客观测量急性精神病症状是具有挑战性的.
- 目前的工具缺乏实时临床监测的速度和客观性.
- 自然语言处理 (NLP) 提供了分析语音模式的潜力.
研究的目的:
- 探索NLP检测到的语言标记,以客观地测量精神分裂症症状严重程度.
- 评估NLP在急性临床环境中对精神病监测的有用性.
- 识别与积极和消极症状相关的语言特征.
主要方法:
- 收集了来自七名住院精神分裂症患者的语音样本.
- 使用SAPS/SANS和TLC等级评估症状.
- 使用机器学习提取语言特征,并分析与症状严重程度的相关性.
主要成果:
- 词汇丰富度降低和语法复杂性与负面症状相关.
- 较低的内容密度和增加的重复预测了积极的症状.
- 机器学习模型准确预测症状严重程度 (高达82%) 和量化语音不一致.
结论:
- NLP可以识别临床相关的语言标志物用于精神分裂症症状监测.
- 这些发现表明,在住院期间评估精神病的客观性提高了.
- 为了实际实施,需要对更大的数据集进行进一步的研究.
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