With the development of advanced screening procedures and techniques, certain limitations of the existing screening processes for disease methodologies and paradigms have been noted. More accurate and less invasive screening methods are needed to diagnose and treat health disorders and diseases before symptoms appear.
Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention is a pivotal reference source that utilizes advanced ICT techniques to solve problems in health data collection, analysis, and interpretation, as well as improve existing health systems for the advanced screening of diseases. Using non-invasive biomedical sensor devices and internet of things technology, this book examines safer methods to accelerate disease detection and effectively treat patients while challenging previously used pre-screening processes. While highlighting topics such as the applications of machine learning, patient safety, diagnostics models, and condition management, this publication is ideally designed for healthcare specialists, researchers in health informatics, industry practitioners, and academics.
Topics Covered
The many academic areas covered in this publication include, but are not limited to:
•Applications of Machine Learning
•Biomedical Sensors
•Body Area Network
•Clinical Decision Support Systems
•Condition Management
•De-Verticalization
•Diagnostics Models
•Evaluation Approaches
•Innovative Approaches
•IOT Screening
•M-Health
•Patient Safety
Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention is a pivotal reference source that utilizes advanced ICT techniques to solve problems in health data collection, analysis, and interpretation, as well as improve existing health systems for the advanced screening of diseases. Using non-invasive biomedical sensor devices and internet of things technology, this book examines safer methods to accelerate disease detection and effectively treat patients while challenging previously used pre-screening processes. While highlighting topics such as the applications of machine learning, patient safety, diagnostics models, and condition management, this publication is ideally designed for healthcare specialists, researchers in health informatics, industry practitioners, and academics.
Topics Covered
The many academic areas covered in this publication include, but are not limited to:
•Applications of Machine Learning
•Biomedical Sensors
•Body Area Network
•Clinical Decision Support Systems
•Condition Management
•De-Verticalization
•Diagnostics Models
•Evaluation Approaches
•Innovative Approaches
•IOT Screening
•M-Health
•Patient Safety