A novel machine learning based framework for developing composite digital biomarkers of disease progression.

ABSTRACT:

Background

Current methods of measuring disease progression of neurodegenerative disorders, including Parkinson's disease (PD), largely rely on composite clinical rating scales, which are prone to subjective biases and lack the sensitivity to detect progression signals in a timely manner. Digital health technology (DHT)-derived measures offer potential solutions to provide objective, precise, and sensitive measures that address these limitations. However, the complexity of DHT datasets and the potential to derive numerous digital features that were not previously possible to measure pose challenges, including in selection of the most important digital features and construction of composite digital biomarkers.

Methods

We present a comprehensive machine learning based framewor

SUBMITTER: Zhai S 

PROVIDER: S-EPMC11760596 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature