Artificial Intelligence in Healthcare

Clinical Decision Support and Modeling
1. Auflage 2025
ISBN: 978-1-5015-2377-9
Verlag: De Gruyter

Buch, Englisch, 450 Seiten, Format (B × H): 178 mm x 229 mm, Gewicht: 879 g

The field of healthcare is being transformed by artificial intelligence (AI). Professionals need to comprehend the potential impact of AI on clinical decision support and epidemiological modeling. This comprehensive guide helps bridge the gap between theory and practice, providing readers with the knowledge and skills needed to leverage AI in healthcare.

The book covers a broad range of topics, from the basics of AI and machine learning to the creation and assessment of clinical decision support systems. It also covers the use of state-of-the-art AI methods for disease surveillance and outbreak prediction. Through a mix of theoretical explanations, practical examples, and hands-on exercises, readers will learn how to prepare and manipulate clinical and epidemiological datasets, build, and implement cutting-edge AI solutions, and address the ethical considerations and challenges of applying AI in healthcare.

What makes this book unique is its combination of expert insights from a practitioner’s perspective, real-world case studies, and a practical approach to walk readers through the process of developing and implementing AI solutions. Additional online resources, like datasets, code samples, and case studies, further enrich the learning experience.

Whether you are a healthcare professional looking to enhance patient outcomes, a data scientist striving to create innovative AI solutions, or a student eager to explore the frontiers of healthcare technology, this book is an essential resource.

Autoren/Hrsg.

Etli, Dominic

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• 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 Künstliche Intelligenz Neuronale Netzwerke
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik

Über Autor(innen)

Dr. Dominic Etli is a clinician and data scientist who applies his dual background in data science and clinical practice to develop machine learning models that optimize clinical delivery and predict clinical outcomes. He holds a Doctor of Nursing Practice (DNP) degree and a Master of Science in Data Science, equipping him with the skills to bridge the gap between healthcare and technology. With over 27 years of experience in healthcare and clinical practice, he has expertise in data analysis, team leadership, and risk assessment, as well as proficiency in Python, PyTorch, SQL, and Azure. He leverages his academic credentials and passion for teaching to instruct and mentor graduate students in clinical epidemiology and advanced pathophysiology at Purdue University and Roseman University of Health Sciences. Dr. Etli designs and delivers courses that emphasize practical data analysis skills and hands-on learning of data science best practices.