Machine Learning in Implementing m Health Apps

4^th International Conference on Digital Health

March 15, 2022 | Webinar

Hongmei Chi

Florida A&M University, USA

Posters & Accepted Abstracts: J Health Med Informat

Abstract :

While IoT devices and smart phones are becoming common in everyone life, mobile health reaches far beyond different health apps and educational tools. Emerging mHealth apps are changing the way patients track information related to diagnosed conditions and make decision about their treatment and choices of different drugs. A new study is examined how mHealth technologies impact of the patients’ health and economic decisions, especially, how to identify patients’ mental health among individuals with chronic medical conditions, such as heart disease, diabetes and cancer. Recently, AI techniques have experienced a resurgence following concurrent advances in computational power, large amounts of data, and theoretical development. ML architectures, a part of a broader family of AI, including deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN), have been applied to fields such as digital health. Many popular mHealth apps adopt Machine Learning (ML) algorithms. This talk aims to explore applications of Machine Learning in designing and implementing mental health application to detect mental health symptoms and risk factors and to develope predictions about disease progression through personalizing and optimizing therapies. This talk will give an overview of ML adopted by realworld mHealth apps and provide the sharing of the database, algorithms, and apps for the research fileds.

Biography :

Dr. Hongmei Chi is Professor of the Department of Computer and Information Sciences at Florida A&M University. She served as director of FAMU center for cybersecurity. She has a broad base of research funding in scientific computation, cybersecurity, and data science. She teaches undergraduate and graduate courses in and network security and cryptography, artificial intelligence and scientific computation. Her research interests span many areas related to Big Data, parallel computing, mobile health security and cyber security. Her work in those areas has been published in top journals and conferences.