Language Modeling Screens Parkinson's Disease with Self-reported Questionnaires.

Abstract

Parkinson's disease (PD) is a growing public health challenge associated with the aging population. Current diagnostic methods rely on motor symptoms and invasive procedures, making early detection difficult. This study established a transferable artificial intelligence (AI) model, Quest2Dx, to analyze health questionnaires to enable low-cost and non-invasive PD diagnosis. Quest2Dx tackles the common challenges of missing responses and required specific modeling for each questionnaire by developing a novel language modeling approach to allow the model transfer across different questionnaires and to enhance the interpretability. Evaluated on the PPMI and Fox Insight datasets, Quest2Dx achieved AUROCs of 0.977 and 0.974, respectively, significantly outperforming existing methods. Additionally, cross-questionnaire validation achieved AUROCs of 0.920 and 0.952, respectively, from PPMI to Fox Insight and vice versa. Quest2Dx also identified key predictors from the list of questions to provide further insights. The validated technology elucidates a promising path for PD screening in primary-care settings.