While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics.
This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You’ll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications.
By the end of this book, you’ll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.

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Spis treści

Applied Machine Learning for Healthcare and Life Sciences Using AWS. Transformational AI implementations for biotech, clinical, and healthcare organizations eBook -- spis treści

• 1. Introducing Machine Learning and the AWS Machine Learning Stack
• 2. Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences
• 3. Machine Learning for Patient Risk Stratification
• 4. Using Machine Learning to Improve Operational Efficiency for Healthcare Providers
• 5. Implementing Machine Learning for Healthcare Payors
• 6. Implementing Machine Learning for Medical Devices and Radiology Images
• 7. Applying Machine Learning to Genomics
• 8. Applying Machine Learning to Molecular Data
• 9. Applying Machine Learning to Clinical Trials and Pharmacovigilance
• 10. Utilizing Machine Learning in the Pharmaceutical Supply Chain
• 11. Understanding Common Industry Challenges and Solutions
• 12. Understanding Current Industry Trends and Future Applications