Buch, Englisch, 286 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 408 g
Reihe: Big Data for Industry 4.0
Buch, Englisch, 286 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 408 g
Reihe: Big Data for Industry 4.0
ISBN: 978-0-367-55497-2
Verlag: Taylor & Francis Ltd (Sales)
This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research.
"Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare.
Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.
Zielgruppe
Academic and Professional
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
- Mathematik | Informatik Mathematik Operations Research
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
- Technische Wissenschaften Technik Allgemein Industrial Engineering
- Technische Wissenschaften Technik Allgemein Technische Zuverlässigkeit, Sicherheitstechnik
Weitere Infos & Material
Part I: Conceptual. 1. Introduction to Big Data. 2. Introduction to Machine Learning. Part II: Application. 3. Machine Learning in Clinical Trials. 4. Deep Learning and Its Biological and Biomedical Applications. 5. Applications of Machine Learning Algorithms to Cancer Data. 6. Pancreatic Cancer Detection by an Integrated Level Set-Based Deep Learning Model. 7. Early and Precision-Oriented Detection of Cervical Cancer. 8. Transformation of mHealth in Society. 9. Artificial Intelligence and Deep Learning for Medical Diagnosis and Treatment. Part III: Ethics. 10. Ethical Issues and Challenges with Artificial Intelligence in Healthcare. 11. Epistemological Issues and Challenges with Artificial Intelligence in Healthcare.