Application of Machine Learning in a Parkinson's Disease Digital Biomarker Dataset Using Neural Network Construction (NNC) Methodology Discriminates Patient Motor Status

Abstract

Parkinson’s disease (PD) patient care is limited by inadequate, sporadic symptom monitoring, infrequent access to care, and sparse encounters with healthcare professionals leading to poor medical decision making and sub-optimal patient health-related outcomes. Recent advances in digital health approaches have enabled objective and remote monitoring of impaired motor function with the promise of profoundly changing the diagnostic, monitoring, and therapeutic landscape in PD. We recently demonstrated that by using a variety of upper limb functional tests iMotor, an artificial intelligence powered, cloud-based digital platform differentiated PD subjects from healthy volunteers (HV). The objective of this paper is to provide preliminary evidence that artificial intelligence systems may allow one to discriminate PD patients from (HV) further and determine different features of the disease within a cohort of PD subjects. The recently introduced Neural Network Construction (NNC) technique was used here to classify data collected by a mobile application (iMotor, Apptomics Inc, Wellesley, MA) into two categories: PD for patients and HV. The method was tested on a series of data previously collected, and the results were compared against more traditional techniques for neural network training. The NNC algorithm discriminated individual PD patients from HVs with 93.11% accuracy and ON vs. OFF state with 76.5% accuracy. Future applications of artificial intelligence-powered digital platforms can enhance clinical care and research by generating rich, reliable and sensitive datasets that can be used for medical decision- making during and between office visits. Additional artificial intelligence-based studies in larger cohorts of patients are warranted.