Development of machine learning: derived mhealth composite biomarkers for trial@home clinical trials

Zhuparris, A. (2024)

Doctoral Thesis

This research focuses on creating composite biomarkers that can classify diagnoses, estimate symptom severity, and detect treatment effects using data from wearable sensors and smartphone applications. The thesis consists of an introduction to machine learning techniques and their use in developing biomarkers for the central nervous system; a narrative review of the relevant literature; and detailed studies on the application of these techniques in various health conditions. Specifically, the research includes observational and cross-sectional studies on facioscapulohumeral muscular dystrophy (FSHD) and major depressive disorder (MDD), demonstrating how smartphone and wearable sensor data can be used to monitor disease severity and progression. Additionally, the research identified the use of a tablet-based finger tapping task to monitor the real-time effects of antiparkinson's drugs on Parkinson's symptom severity. Key findings highlight the potential of mHealth biomarkers to provide continuous, real-time monitoring of patients, which can enhance the accuracy of clinical assessments and potentially reduce the burden on patients and healthcare systems. The thesis also addresses the challenges of variability in mHealth device data and emphasizes the need for robust validation and standardization to ensure the reliability of these biomarkers in clinical settings.

• Machine Learning
• Artificial Intelligence
• mHealth
• Smartphone
• Wearables
• Clinical Biomarkers
• Parkinson's Disease
• FSHD
• Major Depressive Disorder

• All authors: Zhuparris, A.
• Supervisor: Groeneveld, G.J.; Kraaij, W.
• Co-supervisor: Doll, R.J.
• Committee: Cohen, A.F.; Brouwer, A.; Baratchi, M.; Hoogendoorn, M.
• Qualification: Doctor (dr.)
• Awarding Institution: Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University
• Date: 2024-06-13