Healthcare Solutions Using Machine Learning and Informatics

Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, ma...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Gupta, Punit, Kumar Saini, Dinesh, Verma, Rohit
Format: Buch
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence. The wide variety of topics covered include: Artificial Intelligence in healthcare Machine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19 Big data analytics solutions for healthcare data processing Reliable biomedical applications using AI models Intelligent IoT in healthcare The book explains fundamental concepts as well as advanced use cases, illustrating how to apply emerging technologies such as machine learning, AI models, and data informatics in practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academics and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain.
DOI:10.1201/9781003322597