Machine Learning and AI for Healthcare
Big Data for Improved Health OutcomesPaperback Engels 2020 9781484265369
This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.
The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things.
You will understand how machine learning can be used to develop health intelligence–with the aim of improving patient health, population health, and facilitating significant care-payer cost savings.
What You Will Learn
-Understand key machine learning algorithms and their use and implementation within healthcare
-Implement machine learning systems, such as speech recognition and enhanced deep learning/AI
-Manage the complexities of massive data
-Be familiar with AI and healthcare best practices, feedback loops, and intelligent agents
Who This Book Is For
Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
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Chapter 2: Big Data Chapter Goal: To understand data required for learning and how to ensure valid data for outcome veracityNo of pages: 35Sub - 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 complexities3. The key aspects required of data, in particular, validity to ensure that only useful and relevant information4. 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 data6. Challenges faced as part of the use of big data7. 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 applicationsNo of pages: 45Sub - Topics: 1. Introduction – what is learning?2. Differences/similarities between: what is AI, data science, machine learning, deep learning3. History/evolution of learning4. Learning algorithms – popular types/categories, complex examples of machine learning models, applications and their mathematical basis5. Software(s) used for learning6. Code samples
Chapter 4: Machine Learning in HealthcareChapter Goal: A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settings No of pages: 50Sub - 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 uses3. Real-time analysis and analytics4. Machine learning best practices5. Neural networks, ANNs, deep learning6. Code samples
Chapter 5: Evaluating Learning for IntelligenceChapter Goal: To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysisNo of pages: 301. How to evaluate machine learning systems 2. Methodologies for evaluating outputs3. Improving your intelligence4. Advanced analytics5. Real-world examples of evaluations
Chapter 6: Ethics of intelligenceChapter 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: 251. The benefits of big data and machine learning2. 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 outputs5. Do we need to govern our intelligence?6. Example: COVID-19 response and data/privacy sharing
Chapter 7: The Future of HealthcareChapter Goal: Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systemsNo of pages: 301. Evidence-based medicine2. Patient data as the evidence base3. Healthcare disruption fueling innovation4. How generalisations on precise audiences enables personalized medicine5. Impact of data and IoT on realizing personalized medicine6. AI ethics7. Conclusion
Chapter 8: Case studiesChapter Goal: Real world applications of AI and machine/deep learning in healthcareNo of pages: 501. 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
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