Applications of deep learning methods in digital biomarker research using noninvasive sensing data

Area of Science:

• Digital Health and Medicine
• Artificial Intelligence in Healthcare
• Biomarker Discovery

Background:

• Noninvasive digital biomarkers are crucial for digital healthcare due to ease of measurement and raw data utilization.
• Deep learning methods, including representation and supervised learning, are increasingly used for analyzing heterogeneous digital biomarker data.

Purpose of the Study:

• To introduce clinical cases of digital biomarkers and associated deep learning applications.
• To present deep learning methods for integrated analysis of multidimensional heterogeneous data.
• To examine the current status and future directions of digital biomarker research.

Main Methods:

• Survey of research cases applying various data types and modeling methods for digital biomarkers.
• Introduction of deep learning for dimensionality reduction and mode integration in multimodal digital biomarker studies.
• Analysis of clinical cases demonstrating the application of deep learning to digital biomarker data.

Main Results:

• Deep learning enables the integrated analysis of multidimensional, heterogeneous data for digital biomarkers.
• Multimodal integration improves research performance and offers complementarity between different data sources.
• Future research directions focus on combining heterogeneous data sources using deep learning techniques.

Conclusions:

• Integrative digital biomarkers are highly valuable for complex diseases requiring multi-source data.
• This approach facilitates immediate detection and more accurate symptom prediction by capturing subtle patient signals and interactions.
• Deep learning-powered multimodal digital biomarkers represent a significant advancement in personalized and predictive healthcare.