The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases.

Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students.

Coverage:
The many academic areas covered in this publication include, but are not limited to:
•Big Data
•Deep Learning
•Human Behavior Modeling
•Image Data
•Internet of Medical Things
•Internet of Things
•Medical Images
•Medical Sensors
•Mental Healthcare
•Mobile Health
•Multimodal Analysis