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: CRC Press
"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 Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
- Technische Wissenschaften Technik Allgemein Technische Zuverlässigkeit, Sicherheitstechnik
- Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
- Technische Wissenschaften Technik Allgemein Industrial Engineering
- Mathematik | Informatik Mathematik Operations Research
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
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.