Buch, Englisch, 252 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 531 g
Fundamentals and Recent Applications
Buch, Englisch, 252 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 531 g
ISBN: 978-0-367-56442-1
Verlag: CRC Press
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.
Zielgruppe
Academic
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Krankenhausmanagement, Praxismanagement
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
- Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie Medizinische Biotechnologie
Weitere Infos & Material
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 Diseases. 11. Mental Illness and Neurodevelopmental Disorders. 12. Applications and Challenges.