PDF: machine learning in medicine
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"Machine Learning in Medicine" is a comprehensive exploration of how machine learning techniques can be applied to various fields within medicine and healthcare. It addresses the critical role that data analysis and predictive modeling play in the improvement of patient outcomes, efficiency in healthcare delivery, and the development of personalized medicine strategies. With a focus on practical applications and real-world case studies, the book serves both as an educational resource and a reference for healthcare professionals, data scientists, and researchers interested in the intersection of these two rapidly evolving domains.
The bibliographic details of the book include the following: it is authored by Dr. Shalmali S. Kamat and Dr. Michael C. Stojanovic. The book was published by Academic Press in April 2021. The ISBN numbers are 978-0-12-817880-6 (hardcover) and 978-0-12-817881-3 (eBook), ensuring accessibility in multiple formats for diverse audiences.
Topics covered in the book span a wide range of machine learning techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning. It emphasizes ethical considerations in the deployment of AI in medicine, addressing biases in algorithms, data privacy issues, and the importance of interpretability in machine learning models to foster trust among healthcare practitioners and patients alike.
"Machine Learning in Medicine" stands as a significant contribution to both the fields of healthcare and artificial intelligence. It not only presents a detailed account of machine learning methodologies but also highlights the importance of collaboration between healthcare professionals and data scientists. By bridging the gap between technical knowledge and practical application, the book aims to equip its readers with the necessary tools to innovate in their own medical practices and research endeavors.
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