Deep Learning-Driven Sensing Systems for Chronic Disease Management: A Survey

PhD Qualifying Examination


Title: "Deep Learning-Driven Sensing Systems for Chronic Disease Management: 
A Survey"

by

Mr. Chi XU


Abstract:

Chronic diseases represent a significant public health challenge, 
contributing to approximately 40 million deaths annually and accounting for 
more than 70% of global mortality, as reported by the World Health 
Organization (WHO). However, effective management of these diseases remains 
a critical issue worldwide, necessitating innovative solutions. Fortunately, 
the emergence of deep learning and various sensing techniques has led to the 
development of powerful systems that demonstrate substantial potential in 
chronic disease management. Due to their capacity to learn from vast amounts 
of data, identify complex patterns, and make informed decisions that mimic 
human cognitive functions, neural networks are able to excel in tasks such 
as image recognition, natural language processing, and predictive analytics. 
Additionally, different healthcare scenarios present unique requirements, 
indicating that tailored sensors and modalities should be employed to 
address specific needs effectively. To achieve better performance in this 
domain, combinations of deep learning and various sensors are essential for 
optimizing treatment strategies and enhancing patient outcomes.

This survey explores the transformative potential of deep learning-driven 
sensing systems in chronic disease management, emphasizing their ability to 
analyze vast amounts of heterogeneous data for accurate predictions and 
real-time monitoring. We first provide an overview of fundamental concepts 
and recent advancements in deep learning and sensing systems. Next, we will 
give a detailed literature review on specific applications across various 
chronic diseases, including respiratory, cardiovascular, and neurological 
disorders. Furthermore, we discuss our research efforts in respiratory 
disease assessment and intervention. In conclusion, we identify critical 
future directions for enhancing chronic disease management systems. This 
review aims to highlight the significant impact of deep learning and sensing 
technologies on improving patient healthcare outcomes.


Date:                   Monday, 10 February 2025

Time:                   1:00pm - 3:00pm

Venue:                  Room 4472
                        Lifts 25/26

Committee Members:      Prof. Qian Zhang (Supervisor)
                        Dr. Xiaojuan Ma (Chairperson)
                        Prof. Song Guo
                        Dr. Xiaomin Ouyang