Machine Learning in Healthcare
Fundamentals and Recent Applications
- 226 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods, social media, etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions, it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology, variability in medical data, small datasets, and lack of standard guidelines for application of AI. Therefore, the development of new AI/ML tools for various domains of medicine is an ongoing field of research.
Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems.
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Information
Table of contents
- Cover
- Half-Title Page
- Title Page
- Copyright Page
- Dedication
- Contents
- List of Figures
- List of Tables
- Preface
- Acknowledgments
- Author Bio
- 1 Biostatistics
- 2 Probability Theory
- 3 Medical Data Acquisition and Pre-processing
- 4 Medical Image Processing
- 5 Bio-signals
- 6 Feature Extraction
- 7 Introduction to Machine Learning
- 8 Cancer Detection: Breast Cancer Detection Using Mammography, Ultrasound and Magnetic Resonance Imaging (MRI)
- 9 Sickle Cell Disease Management: A Machine Learning Approach
- 10 Detection of Pulmonary Disease
- 11 Mental Illness and Neurodevelopmental Disorders
- 12 Applications and Challenges
- Index