The key highlights and insights from the content are:
Parkinson's disease (PD) is a prevalent neurodegenerative disorder that is challenging to detect early due to symptom heterogeneity and the lack of early-stage biomarkers. Language impairment can present in the prodromal phase and precede motor symptoms, suggesting that a linguistic-based approach could serve as a diagnostic method for incipient PD.
The study evaluates the application of state-of-the-art large language models, including BERT, XLNet, GPT-2, and text-embedding models from OpenAI, to detect PD automatically from spontaneous speech. The models generate high-dimensional linguistic feature spaces that are then used to train a support vector machine (SVM) classifier.
The results show that the text-embedding-3 models outperform the other evaluated models, achieving up to 73% accuracy in detecting PD, which is an improvement over the previous research using BERT (66% accuracy).
The performance of the text-embedding-3 models is largely independent of the dimensionality of the embedding output, suggesting that the better performance is due to the intrinsic architecture of the large language models rather than just the increased dimensionality.
The study highlights the potential of using spontaneous speech as a classifiable biomarker for PD through linguistic representation in text embeddings. It also discusses the limitations of the small dataset size, the potential for misdiagnosis in the dataset, and the need for further research to address these challenges.
Future research directions include exploring different prompts and mediums for conversational tasks, incorporating longitudinal data to track the progression of linguistic markers, and developing ensemble methods that combine acoustic and linguistic features to improve the accuracy of PD detection.
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by Jonathan Cra... lúc arxiv.org 04-09-2024
https://arxiv.org/pdf/2404.05160.pdfYêu cầu sâu hơn