[python homework AI graduation design blog] Artificial intelligence Medical New Book Download: Machine Learning and AI for Healthcare-2019.pdf-Alibaba Cloud Developer Community

Download Address: https://itbooks.pipipan.com/fs/18113597-335448646

explore the theory and practical application of artificial intelligence (AI) and medical machine learning. This book provides an overview of machine learning algorithms, architecture design, and learning applications in healthcare and Big Data challenges.

You will discover the moral implications of health care data analysis and the future of AI in population and patient health optimization. You will also create a machine learning model, evaluate performance, and implement results within your organization.

Machine learning and healthcare artificial intelligence provide technologies on how to apply machine learning within an organization and evaluate the efficacy, applicability, and efficiency of AI applications. These are illustrated through major case studies, including how to redefine chronic diseases through patient-led data learning and the internet of things.

What will you learn

  • in-depth understanding of key machine learning algorithms and their use and implementation in wider healthcare
  • implement machine learning systems, such as speech recognition and enhanced deep learning/AI
  • select learning methods/algorithms and adjustments for medical care
  • recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops, and intelligent agents

who does this book apply

health care professionals interested in how machine learning is used to develop health intelligence-aimed at improving patient health, population health and promoting significant care cost savings.

Chapter 1: what is artificial intelligence chapter objective: Introduction to books and topics page number: 10sub-Topics1. What are artificial intelligence, data science, machine and deep learning 2. Case 3 learned from data. Evolution of Big Data/learning/analysis 3.0 4. Practical example of how to use data for learning in a health care environment 5. Conclusion

chapter 2: Data chapter objective: understand the data needed for learning and how to ensure the accuracy of the results. Valid data pages: 30 sub-topics 1. What is data, data sources and what types of data are there? It is rarely related to the advantages and disadvantages of big data and such datasets. Structured and unstructured data. 2. Key aspects of data required, especially effectiveness, to ensure only useful and relevant information 3. How to use big data for learning (use cases) 4. Converting data into information-how to collect data that can be used to improve health results and how to collect such data example 5. Challenges as part of the use of big data 6. Data governance

chapter 3: What is machine learning? Chapter objective: introduce machine learning, identify/uncover the mystery of learning types, and provide information about popular algorithms and their applications page: 45 sub-topic: 1. Introduction-What is learning? 2. Differences/similarities between people, data science, machine learning, deep learning 3. History/evolution of learning 4. Learning algorithms-popular types/categories, applications and mathematical foundations 5. Software for Learning

chapter 4: medical machine learning chapter objective: to fully understand the key concepts related to the learning system and the practical application of machine learning in medical institutions page: 50 sub-topics: 1. Understand tasks, performance, and experience to optimize algorithms and results 2. Identifying algorithms for healthcare applications: predictive analysis, perspective analysis, reasoning, modeling, probability estimation, NLP, etc. and common uses 3. Real-time analysis and Analysis 4. Machine learning best practices 5. Neural network, artificial neural network, deep learning

chapter 5: evaluating intelligence learning chapter objective: to understand how to evaluate learning algorithms and how to choose the best evaluation technology/method for analysis page: 101. How to evaluate machine learning system 2. Method 3 for evaluating output. Improve your Intelligence 4. Advanced analysis

chapter 6: intellectual ethics chapter objective: understand the obstacles that artificial intelligence/machine learning must solve, and overcome the obstacles at the micro and macro levels at the same time to enhance health intelligence page number: 251. Benefits of big data and machine learning 2. Disadvantages of big data and machine learning-who owns data, distributes data, and should tell patients/people what results are (e.g. data showing cancer risk) 3. Is the data good or bad? 4. Topics to be addressed to ensure simplicity, efficiency and safety of output do we need to manage our intelligence?

Chapter 7: Future chapters of health care objective: overview the direction of artificial intelligence and machine/Deep Learning in health care and the future application of intelligent systems page: 301. Evidence-Based Medicine 2. Patient data as evidence basis 3. Health care interruption promotes innovation 4. How to realize personalized medical treatment by summarizing accurate audience 5. The impact of data and the internet of things on personalized medical care 6. How about ethics? 7. Conclusion

chapter 8: Case Study Chapter objective: the real application of artificial intelligence and machine/deep learning in the field of health care Page: 201. Real case studies of organizations implementing machine learning and challenges, methods, algorithms, and analysis for determining optimal performance/results

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