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: Paperback / softback
Length: 252 pages
Publication date: 16 July 2022
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 levels, which can be an early indicator of various health conditions.
• Automatic diagnosis of diseases from X-ray images: Utilizing advanced image processing algorithms to analyze medical images and identify potential diseases with high accuracy.
• Intelligent diagnosis of glaucoma from clinical eye examination data: Using machine learning algorithms to analyze eye examination data and accurately detect glaucoma, a serious eye disease.
• Prediction of protein-coding genes from big genome data: Analyzing large genomic datasets to identify potential genes associated with diseases, aiding in disease diagnosis and personalized medicine.
• 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, facilitating disease diagnosis and research.
• Prediction of drug-target interactions: Analyzing drug-protein interactions and predicting potential drug-target interactions, aiding in drug discovery and 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, making it a valuable tool for exploring cutting-edge developments in the field. 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 expertise in the field, leading to improved patient outcomes and a better understanding of disease mechanisms.

In summary, this book offers a comprehensive and insightful exploration of the applications of machine learning and data mining techniques in disease tracking and prevention. It serves as a valuable resource for computer scientists, researchers, healthcare professionals, and industry practitioners alike, empowering them to leverage the power of AI and ML to advance healthcare and improve human well-being.

Weight: 421g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030767341
Edition number: 1st ed. 2022