성보의학서적: 국내외 의학서적 전문 온라인서점

의학서적전문 "성보의학서적"의 신간의학도서입니다.

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.

Provides an overview of machine learning, both for a clinical and engineering audience
Summarize recent advances in both cardiovascular medicine and artificial intelligence
Discusses the advantages of using machine learning for outcomes research and image processing
Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

-도서목차-

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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