Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson's disease.

Abstract

Parkinson's disease (PD) is one of the first diseases where digital biomarkers demonstrated excellent performance in differentiating disease from healthy individuals. However, no study has systematically compared and leveraged multiple types of digital biomarkers to predict PD. Particularly, machine learning works on the fine-motor skills of PD are limited. Here, we developed deep learning methods that achieved an AUC (Area Under the receiver operator characteristic Curve) of 0.933 in identifying PD patients on 6418 individuals using 75048 tapping accelerometer and position records. Performance of tapping is superior to gait/rest and voice-based models obtained from the same benchmark population. Assembling the three models achieved a higher AUC of 0.944. Notably, the models not only correlated strongly to, but also performed better than patient self-reported symptom scores in diagnosing PD. This study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.

• Authors: Kaiwen Deng, Yueming Li, Hanrui Zhang, Jian Wang, Roger L Albin, Yuanfang Guan
• Journal: Communications biology (Commun Biol) Vol. 5 Issue 1 Pg. 58 (01 17 2022) ISSN: 2399-3642 [Electronic] England
• PMID: 35039601 (Publication Type: Comparative Study, Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
• Copyright: © 2022. The Author(s).

Chemical References

• Biomarkers

Topics

• Adult
• Aged
• Aged, 80 and over
• Area Under Curve
• Biomarkers (analysis)
• Female
• Humans
• Male
• Middle Aged
• Parkinson Disease (diagnosis)
• Self Report
• Wearable Electronic Devices (statistics & numerical data)
• Young Adult