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Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes
Chapter 1: What is Artificial IntelligenceChapter Introduction to book and topics to be covered No of pages 10Sub -Topics1. What is AI, data science, machine and deep learning2. The case for learning from data3. Evolution of big data/learning/Analytics 3.04. Practical examples of how data can be used to learn within healthcare settings5. Conclusion
Chapter 2: DataChapter To understand data required for learning and how to ensure valid data for outcome veracityNo of 30Sub - 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. The key aspects required of data, in particular, validity to ensure that only useful and relevant information3. How to use big data for learning (use cases)4. Turning data into information - how to collect data that can be used to improve health outcomes and examples of how to collect such data5. Challenges faced as part of the use of big data6. Data governance
Chapter 3: What is Machine learning?Chapter To introduce machine learning, identify/demystify types of learning and provide information of popular algorithms and their applicationsNo of 45Sub - 1. Introduction - what is learning?2. Differences/similarities what is AI, data science, machine learning, deep learning3. History/evolution of learning4. Learning algorithms - popular types/categories, applications and their mathematical basis5. Software(s) used for learning
Chapter 4: Machine learning in healthcareChapter A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settings No of 50Sub - 1. Understanding Tasks, Performance and Experience to optimize algorithms and outcomes 2. Identification of algorithms to be used in healthcare applications 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 learning
Chapter 5: Evaluating learning for intelligenceChapter To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysisNo of 101. How to evaluate machine learning systems 2. Methodologies for evaluating outputs3. Improving your intelligence4. Advanced analytics
Chapter 6: Ethics of intelligenceChapter 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 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?
Chapter 7: The future of healthcareChapter Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systemsNo of 301.
Chapter 2: DataChapter To understand data required for learning and how to ensure valid data for outcome veracityNo of 30Sub - 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. The key aspects required of data, in particular, validity to ensure that only useful and relevant information3. How to use big data for learning (use cases)4. Turning data into information - how to collect data that can be used to improve health outcomes and examples of how to collect such data5. Challenges faced as part of the use of big data6. Data governance
Chapter 3: What is Machine learning?Chapter To introduce machine learning, identify/demystify types of learning and provide information of popular algorithms and their applicationsNo of 45Sub - 1. Introduction - what is learning?2. Differences/similarities what is AI, data science, machine learning, deep learning3. History/evolution of learning4. Learning algorithms - popular types/categories, applications and their mathematical basis5. Software(s) used for learning
Chapter 4: Machine learning in healthcareChapter A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settings No of 50Sub - 1. Understanding Tasks, Performance and Experience to optimize algorithms and outcomes 2. Identification of algorithms to be used in healthcare applications 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 learning
Chapter 5: Evaluating learning for intelligenceChapter To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysisNo of 101. How to evaluate machine learning systems 2. Methodologies for evaluating outputs3. Improving your intelligence4. Advanced analytics
Chapter 6: Ethics of intelligenceChapter 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 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?
Chapter 7: The future of healthcareChapter Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systemsNo of 301.
396 pages, Paperback
Published February 5, 2019
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Displaying 1 of 1 review
March 24, 2019
After the first chapter of this book, I was ready to put it down and regret the money I spent on it. It seemed to walk over ground that I've already covered as a researcher in medical informatics. Fortunately, I continued, for I came to learn a lot from this author. Although not as succinctly written as academic papers, this book is thoroughly researched and comments on an emerging field - the intersection of healthcare and software. It also comments on this from a British perspective. I am used to reading Americans comment on this field, but comments from a Brit who possesses experience in the field is particularly interesting to me.
The author's experience in this field is particular to Type-2 Diabetes. It is quite obvious that his research tilts towards diabetes. I would like to hear more from this author about work that's being done on other major diseases like HIV/AIDS, malaria, emerging diseases, cystic fibrosis, etc. That is a tall order to ask, I understand, and much work needs to be done for this to be the case. Nonetheless, this is the broad frontier that we now face between medicine and computers.
I'm glad that Panesar added his voice to the effort to leverage computers to fight disease, and I'm glad that I took the time to listen.
The author's experience in this field is particular to Type-2 Diabetes. It is quite obvious that his research tilts towards diabetes. I would like to hear more from this author about work that's being done on other major diseases like HIV/AIDS, malaria, emerging diseases, cystic fibrosis, etc. That is a tall order to ask, I understand, and much work needs to be done for this to be the case. Nonetheless, this is the broad frontier that we now face between medicine and computers.
I'm glad that Panesar added his voice to the effort to leverage computers to fight disease, and I'm glad that I took the time to listen.
Displaying 1 of 1 review