The linguistic markers detected by large language models can be utilized to develop a regression model for assessing Parkinson's disease progression by capturing the evolving patterns of language impairment over time. These markers, which encompass morphology, syntax, semantics, and pragmatics, can serve as indicators of cognitive decline and motor-speech impairment associated with Parkinson's disease. By tracking changes in these linguistic features longitudinally, a regression model can be trained to predict the progression of the disease based on speech data.
The regression model can be designed to correlate specific linguistic patterns with different stages of Parkinson's disease, allowing for the prediction of disease severity and progression. By analyzing how linguistic markers evolve over time in individuals with Parkinson's disease, the regression model can provide insights into the trajectory of the disease and potentially forecast future cognitive and motor impairments. This approach can offer a non-invasive and continuous monitoring tool for assessing disease progression, complementing traditional clinical evaluations.
Furthermore, the regression model can be fine-tuned using additional clinical data such as Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) scores, time post-diagnosis, and other relevant metrics to enhance its predictive accuracy. By integrating linguistic markers with clinical information, the regression model can provide a comprehensive assessment of Parkinson's disease progression based on speech data, offering valuable insights for healthcare professionals and researchers.