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.

The authors have declared no competing interest.

This research was funded in part by the training grant T32AG078123, NSF CAREER award 2046708, and NIH grants U01 AG066833, U01 AG068057, and P30 AG073105. The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Parkinsons Progression Markers Initiative PPMI - https://www.ppmi-info.org/ Fox Insight -https://foxden.michaeljfox.org/insight/explore/insight.jsp

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The data that support the findings of this study are available from PPMI and Fox Insight but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of PPMI and Fox Insight.

https://www.ppmi-info.org/

https://foxden.michaeljfox.org/insight/explore/insight.jsp