Castelli Matilde, Sousa Mario, Vojtech Illner, Single Michael, Amstutz Deborah, Maradan-Gachet Marie Elise, Magalhães Andreia D, Debove Ines, Rusz Jan, Martinez-Martin Pablo, Sznitman Raphael, Krack Paul, Nef Tobias
ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland.
Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland.
NPJ Parkinsons Dis. 2025 Apr 18;11(1):79. doi: 10.1038/s41531-025-00939-8.
Over the past decade, neuropsychiatric fluctuations in Parkinson's disease (PD) have been increasingly recognized for their impact on patients' quality of life. Speech, a complex function carrying motor, emotional, and cognitive information, offers potential insights into these fluctuations. While previous studies have focused on acoustic analysis to assess motor speech disorders reliably, the potential of linguistic patterns associated with neuropsychiatric fluctuations in PD remains unexplored. This study analyzed the content of spontaneous speech from 33 PD patients in ON and OFF medication states, using machine learning and large language models (LLMs) to predict medication states and a neuropsychiatric state score. The top-performing model, the LLM Gemma-2 (9B), achieved 98% accuracy in differentiating ON and OFF states and its predicted scores were highly correlated with actual scores (Spearman's ρ = 0.81). These methods could provide a more comprehensive assessment of PD treatment effects, allowing remote neuropsychiatric symptom monitoring via mobile devices.
在过去十年中，帕金森病（PD）的神经精神波动因其对患者生活质量的影响而越来越受到认可。言语作为一种承载运动、情感和认知信息的复杂功能，为了解这些波动提供了潜在的线索。虽然先前的研究集中在声学分析上，以可靠地评估运动性言语障碍，但与PD神经精神波动相关的语言模式的潜力仍未得到探索。本研究分析了33名PD患者在服药和未服药状态下的自发言语内容，使用机器学习和大语言模型（LLMs）来预测服药状态和神经精神状态评分。表现最佳的模型，即大语言模型Gemma-2（9B），在区分服药和未服药状态时准确率达到98%，其预测分数与实际分数高度相关（斯皮尔曼相关系数ρ = 0.81）。这些方法可以对PD治疗效果进行更全面的评估，从而通过移动设备实现远程神经精神症状监测。
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