Integrating Health and Non-Health Big Data to Predict Infection and Deterioration from Respiratory Infectious Diseases.
Integrating Health and Non-Health Big Data to Predict Infection and Deterioration from Respiratory Infectious Diseases.
Yosi Levi, PhD. student at the department of Industrial Engineering
Advisor:Prof. Dan Yamin
Abstract:
Infectious diseases continue to pose significant global health challenges, affecting recovery trajectories, acute care management, and vaccination strategies. This study integrates advanced data sources, including smartwatch and smartphone data, clinical records, and biological markers, to address these challenges. The study examines the prolonged physiological effects of infections such as COVID-19, influenza, and group A streptococcus, providing objective assessments of recovery. Additionally, a machine learning model is introduced to predict mortality risk in hospitalized COVID-19 patients, facilitating timely medical interventions. Furthermore, the research explores the use of wearable devices to assess vaccine side effect severity and compare physiological responses to mRNA COVID-19 and influenza vaccines. By leveraging smart technologies, clinical and biological data, and machine learning, this work advances personalized healthcare, enhances public health strategies, and contributes to more effective disease monitoring and management.
Bio:
Yosi Levi, Ph.D. student in the Department of Industrial Engineering at Tel Aviv University, specializing in Data Science. He holds a B.Sc. and M.Sc. in Mechanical Engineering from Ben-Gurion University. His research, supervised by Prof. Dan Yamin, focuses on integrating wearable and smart device technologies into personalized healthcare, advancing methods for recovery monitoring, early detection, and vaccine response assessment.
