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· 제목 : Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes (Paperback, 2)
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9781484265369
· 쪽수 : 407쪽
· 출판일 : 2020-12-16
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9781484265369
· 쪽수 : 407쪽
· 출판일 : 2020-12-16
목차
Chapter 1: Introduction: Learning for Healthcare
Chapter Goal: Introduction to book and topics to be covered
No of pages 10
Sub -Topics
1. What is AI, data science, machine and deep learning
2. The case for learning from data
3. Evolution of big data/learning/Analytics 3.0
4. Practical examples of how data can be used to learn within healthcare settings
5. Conclusion
Chapter 2: Big Data
Chapter Goal: To understand data required for learning and how to ensure valid data for outcome veracity
No of pages: 35
Sub - Topics
1. What is data, sources of data and what types of data is there? little vs big data and the advantages/disadvantages with such data sets. Structured vs. unstructured data.
2. Massive data - management and complexities
3. The key aspects required of data, in particular, validity to ensure that only useful and relevant information
4. How to use big data for learning (use cases)
5. Turning data into information ? how to collect data that can be used to improve health outcomes and examples of how to collect such data
6. Challenges faced as part of the use of big data
7. Data governance
Chapter 3: What is Machine learning?
Chapter Goal: To introduce machine learning, identify/demystify types of learning and provide information of popular algorithms and their applications
No of pages: 45
Sub - Topics:
1. Introduction ? what is learning?
2. Differences/similarities between: what is AI, data science, machine learning, deep learning
3. History/evolution of learning
4. Learning algorithms ? popular types/categories, complex examples of machine learning models, applications and their mathematical basis
5. Software(s) used for learning
6. Code samples
Chapter 4: Machine Learning in Healthcare
Chapter Goal: A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settings
No of pages: 50
Sub - Topics:
1. Understanding Tasks, Performance and Experience to optimize algorithms and outcomes
2. Identification of algorithms to be used in healthcare applications for: predictive analysis, perspective analysis, inference, modeling, probability estimation, NLP etc and common uses
3. Real-time analysis and analytics
4. Machine learning best practices
5. Neural networks, ANNs, deep learning
6. Code samples
Chapter 5: Evaluating Learning for Intelligence
Chapter Goal: To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysis
No of pages: 30
1. How to evaluate machine learning systems
2. Methodologies for evaluating outputs
3. Improving your intelligence
4. Advanced analytics
5. Real-world examples of evaluations
Chapter 6: Ethics of intelligence
Chapter Goal: To understand the hurdles that must be addressed in AI/machine learning and also overcome on both a micro- and macro-level to enable enhanced health intelligence
No of pages: 25
1. The benefits of big data and machine learning
2. The disadvantages of big data and machine learning ? who owns the data, distributing the data, should patients/people be told what the results are (e.g. data demonstrates risk of cancer)
3. Data for good, or data for bad?
4. Topics that require addressing in order to ensure ease, efficiency and safety of outputs
5. Do we need to govern our intelligence?
6. Example: COVID-19 response and data/privacy sharing
Chapter 7: The Future of Healthcare
Chapter Goal: Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systems
No of pages: 30
1. Evidence-based medicine
2. Patient data as the evidence base
3. Healthcare disruption fueling innovation
4. How generalisations on precise audiences enables personalized medicine
5. Impact of data and IoT on realizing personalized medicine
6. AI ethics
7. Conclusion
Chapter 8: Case studies
Chapter Goal: Real world applications of AI and machine/deep learning in healthcare
No of pages: 50
1. Real world case studies of organizations implementing machine learning and the challenges, methodologies, algorithms and analytics used to determine optimal performance/outcomes
2. COVID-related case studies: how data was used, how rapid interventions were deployed, agile development methodolodies
