Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language models
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Parkinson's Disease |
| Online Access: | https://doi.org/10.1038/s41531-025-00939-8 |
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| author | Matilde Castelli Mario Sousa Illner Vojtech Michael Single Deborah Amstutz Marie Elise Maradan-Gachet Andreia D. Magalhães Ines Debove Jan Rusz Pablo Martinez-Martin Raphael Sznitman Paul Krack Tobias Nef |
| author_facet | Matilde Castelli Mario Sousa Illner Vojtech Michael Single Deborah Amstutz Marie Elise Maradan-Gachet Andreia D. Magalhães Ines Debove Jan Rusz Pablo Martinez-Martin Raphael Sznitman Paul Krack Tobias Nef |
| author_sort | Matilde Castelli |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-373c7b6f9bd84809b89bc31f5b2aae28 |
| institution | OA Journals |
| issn | 2373-8057 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Parkinson's Disease |
| spelling | doaj-art-373c7b6f9bd84809b89bc31f5b2aae282025-08-20T02:17:49ZengNature Portfolionpj Parkinson's Disease2373-80572025-04-0111111110.1038/s41531-025-00939-8Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language modelsMatilde Castelli0Mario Sousa1Illner Vojtech2Michael Single3Deborah Amstutz4Marie Elise Maradan-Gachet5Andreia D. Magalhães6Ines Debove7Jan Rusz8Pablo Martinez-Martin9Raphael Sznitman10Paul Krack11Tobias Nef12ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation Group, University of BernDepartment of Neurology, Bern University Hospital and University of BernDepartment of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in PragueARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation Group, University of BernDepartment of Neurology, Bern University Hospital and University of BernDepartment of Neurology, Bern University Hospital and University of BernDepartment of Neurology, Bern University Hospital and University of BernDepartment of Neurology, Bern University Hospital and University of BernDepartment of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in PragueCenter for Networked Biomedical Research in Neurodegenerative Diseases (CIBERNED), Carlos III Institute of HealthARTORG Center for Biomedical Engineering Research, AIMI, University of BernDepartment of Neurology, Bern University Hospital and University of BernARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation Group, University of BernAbstract 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.https://doi.org/10.1038/s41531-025-00939-8 |
| spellingShingle | Matilde Castelli Mario Sousa Illner Vojtech Michael Single Deborah Amstutz Marie Elise Maradan-Gachet Andreia D. Magalhães Ines Debove Jan Rusz Pablo Martinez-Martin Raphael Sznitman Paul Krack Tobias Nef Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language models npj Parkinson's Disease |
| title | Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language models |
| title_full | Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language models |
| title_fullStr | Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language models |
| title_full_unstemmed | Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language models |
| title_short | Detecting neuropsychiatric fluctuations in Parkinson’s Disease using patients’ own words: the potential of large language models |
| title_sort | detecting neuropsychiatric fluctuations in parkinson s disease using patients own words the potential of large language models |
| url | https://doi.org/10.1038/s41531-025-00939-8 |
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