Machine Learning in Cardiovascular Medicine

by Subhi J. Al'Aref M.D., Gurpreet Singh, Lohendran Baskaran, Dimitri Metaxas

Overview

Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine.

Product Details

ISBN-13:	9780128202739
Publisher:	Elsevier Science
Publication date:	11/27/2020
Pages:	454
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

Dr. Subhi Al’Aref is an Instructor in Medicine and an Instructor of Medicine in Radiology at Weill Cornell Medicine and an Assistant Attending Physician at the NewYork-Presbyterian Hospital. Dr. Al’Aref was born and raised in Jerusalem, where he finished his primary and secondary education. He subsequently performed his premedical and medical training at Weill Cornell Medical College in Qatar, and earned his M.D. in 2008. He completed his training in Internal Medicine Residency, Cardiovascular Disease Fellowship, Interventional Cardiology and Preventative Cardiology at The NewYork-Presbyterian Hospital/Weill Cornell Medicine in New York City. He is board certified in Internal Medicine, Cardiovascular Disease, Interventional Cardiology, Vascular Medicine, Echocardiography and Nuclear Cardiology.

Dr. Gurpreet Singh is a Cognitive Software Engineer at Weill Cornell Medicine based in New York. He obtained his bachelor’s degree in Biotechnology in 2012 and have received a scholarship to pursue his PhD at National University of Singapore, where his research project focused Machine Learning based Clinical Decision Support System for Neurodegenerative Diseases (MCADS-ND). His current work includes developing a neural network based algorithm for End-to-End echocardiogram segmentation and analysis, as well as developing a standalone software for simplified machine learning interface (SimplyClassify).

Dr. Lohendran Baskaran is a Visiting Assistant Professor of Research in Radiology at Weill Cornell Medicine, New York, and is a Consultant Cardiologist with the Department of Cardiology at the National Heart Centre Singapore. Dr. Baskaran obtained his MBBS and his BSc in Medical Physics from University College London and performed his initial medical training and MRCP in London. Currently, he is actively involved in research, teaching and clinical duties, where his research focuses on non-invasive cardiac imaging, specifically cardiac CT and Nuclear Cardiology. As an advocate for cardiac wellbeing, he fundraises for research and patients in need, and was co-chair of the inaugural NHCS Heart to Heart Gala. He is also a certified Exercise Specialist with accreditation from the American College of Sports Medicine.

Dimitri Metaxas became an assistant professor in the Computer and Information Science Department at the University of Pennsylvania and director of the VAST Lab. From January 1998 to September 2001 he was a tenured associate professor in the same department. In September 2001, he moved to Rutgers as a professor in the Department of Computer Science at Rutgers University. Since July 2007, Metaxas is a professor II (distinguished professor) and since 2013, he is the chair of the same department. Since 2001, he has founded and has been directing the Center for Computational Biomedicine, Imaging and Modeling (CBIM).

Table of Contents

1. Technological Advances within Digital Medicine
2. An Overview of Artificial Intelligence: Basics and State-of-the-Art Algorithms
3. Machine Learning for Predictive Analytics
4. Deep Learning for Biomedical Applications
5. Generative Adversarial Network for Cardiovascular Imaging
6. Natural Language Processing
7. Contemporary Advances in Medical Imaging
8. Ultrasound and Artificial Intelligence
9. Computed Tomography and Artificial Intelligence
10. Magnetic Resonance Imaging and Artificial Intelligence
11. Nuclear Imaging and Artificial Intelligence
12. Radiomics in Cardiovascular Imaging: Principles and Clinical Implications
13. Automated Interpretation of Electrocardiographic Tracings
14. Machine Learning in Cardiovascular Genomics, Proteomics, and Drug Discovery
15. Wearable Devices and Machine Learning Algorithms for Cardiovascular Health Assessment
16. The Future of Artificial Intelligence in Healthcare
17. Ethical and Legal Challenges

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From the Publisher

Addresses the expanding applications of machine learning in healthcare, specifically within cardiovascular medicine