Tracking and Preventing Diseases with Artificial Intelligence

This book provides an overview of machine learning and data mining techniques for tracking and preventing diseases, covering stress level identification, automatic diagnosis, intelligent diagnosis, gene prediction, disease detection, information retrieval, and drug-target interactions. It is suitable for computer scientists and researchers and is used as a reference book and tutorial for medical experts.

Format: Hardback
Length: 252 pages
Publication date: 15 July 2021
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the fascinating realm of machine learning and data mining techniques, exploring their pivotal role in tracking and preventing diseases. It encompasses a wide range of topics, including:

• Stress level identification from speech: Analyzing speech patterns to detect emotional stress and its potential impact on health.
• Automatic diagnosis of diseases from X-ray images: Utilizing advanced image processing algorithms to identify abnormalities and diagnose diseases with high accuracy.
• Intelligent diagnosis of glaucoma from clinical eye examination data: Leveraging machine learning to analyze eye examination data and accurately detect glaucoma, a serious eye condition.
• Prediction of protein-coding genes from big genome data: Analyzing large genetic datasets to identify genes associated with diseases and potential drug targets.
• Disease detection through microscopic analysis of blood cells: Employing advanced microscopy techniques to analyze blood cells and detect diseases at the cellular level.
• Information retrieval from electronic medical records using named entity recognition approaches: Extracting relevant information from medical records using natural language processing techniques.
• Prediction of drug-target interactions: Analyzing drug-gene interactions and predicting potential drug-target interactions for drug development.

Designed for computer scientists with a bachelor's degree in computer science, this book serves as an invaluable resource for teaching advanced AI for Medicine or AI for Health Care graduate courses. It also provides researchers with a comprehensive overview of the state-of-the-art algorithms addressing various biomedical domains. Furthermore, industry practitioners interested in implementing AI techniques to analyze diseases can benefit from the detailed explanations and practical examples provided in this book.

Medical institutions extensively use this book as a reference material, conducting tutorials for medical experts to demonstrate how advanced AI and ML techniques contribute to disease diagnosis and prediction. By leveraging these powerful tools, healthcare professionals can enhance their understanding and effectiveness in disease management.

In summary, this book offers a comprehensive and insightful exploration of machine learning and data mining techniques in the field of disease tracking and prevention. It serves as a valuable resource for computer scientists, researchers, industry practitioners, and medical professionals alike, empowering them to leverage the power of AI and ML to improve healthcare outcomes and enhance patient care.

Weight: 576g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030767310
Edition number: 1st ed. 2022