AI in Healthcare (AIH301m)
Vietnamese title: Ứng dụng TTNT trong chăm sóc sức khỏe
Credits: 3
Prerequisites: No

Description:
Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. The aim of this unit is to introducethe current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. Students will be guided to analyze data on patient, such as diseases, medications, tests, or valuable health information, and get a sense of how AI could transform patient care and diagnoses.

Learning outcomes:
Learners should be able to describe the structure of healthcare systems, AI, and machine learning.
The major challenges and the issues in efforts to improve healthcare delivery and system.
How to apply a framework for medical data mining, makes good research questions, and construct a data mining workflow.
Define important relationships between the fields of machine learning, biostatistics, and computer programming.
The machine learning models in dynamic medical practices and discontinuous timelines impact clinical machine learning applications.
Principles and practical considerations for integrating AI into clinical workflows, improving the quality of healthcare applications.
Learners should be able to understand AI-emerging technologies in the fields of healthcare.
The ethical issues for the combination of healthcare system and AI.

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises, assignments and labs given by instructor in class or at home and submit on time
- Use laptop in class only for learning purpose
- Promptly access to the FU FLM at https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
No

Materials:
description: https://www.coursera.org/specializations/ai-healthcare
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/intro-to-healthcare?specialization=ai-healthcare
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/introduction-clinical-data?specialization=ai-healthcare
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/fundamental-machine-learning-healthcare?specialization=ai-healthcare
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/evaluations-ai-applications-healthcare?specialization=ai-healthcare
author: Coursera
publisher: Coursera
description: https://www.coursera.org/specializations/healthcare-it
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/ai-for-everyone
author: Coursera
publisher: Coursera
description: - PowerPoint Slides
description: - Assignments 1
description: - Assignments 2
description: - Quiz for Progress Tests

Assessments:
category: Assignment 1
type: on-going
weight: 10.0
duration: 45'
question_type: CLO 1, CLO 2, CLO 8
no_question: Group presentation for the selected article
knowledge_and_skill: 1
grading_guide: - cover content 1-6
note: in class, by group presentation
category: Assignment 2
type: on-going
weight: 10.0
duration: 45'
question_type: CLO 4, CLO 5
no_question: Group presentation for the selected article
knowledge_and_skill: 1
grading_guide: - cover content 16-21
note: in class, by group presentation
category: Assignment 3
type: on-going
weight: 10.0
duration: 45'
question_type: CLO 6, CLO 7
no_question: Group presentation for the selected article
knowledge_and_skill: 1
grading_guide: - cover content 23-28
note: in class, by group presentation
category: Progress test 1
type: on-going
weight: 15.0
duration: 20'-30'
question_type: CLO 1, CLO 2, CLO 3, CLO 8
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 20
grading_guide: - cover content 1-14
note: in class, by instructor
category: Progress test 2
type: on-going
weight: 15.0
duration: 20'-30'
question_type: CLO 4, CLO 5, CLO 6, CLO 7
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 20
grading_guide: - cover content 16-28
note: in class, by instructor
category: Final exam
type: Final exam
weight: 40.0
duration: 60'
question_type: CLO 1 - 8
no_question: Multiple choicesMarked by Computer
knowledge_and_skill: 50
grading_guide: concepts, algorithms; all studied slides
note: by exam board,
using computer

Schedule:
session: Course introduction:
- Introduce the materials of the courses and how to asses these materials
- Introduce the assignments and how to pass
- Introduce CMS and the way to log on to submit the assignments http://cms.btec.fpt.edu.vn and class forum with enroll key.
learning_outcomes: IT
materials: drive.google.com
session: 1.1. Overview of Health Care Systems and Key Challenges They Face:
1.1.1. The Basic Structure of Health Care Systems
learning_outcomes: T
materials: mat2
session: 1.1. Overview of Health Care Systems and Key Challenges They Face:
1.1.2. Three Key Challenges Facing Health Care Systems
learning_outcomes: T
materials: drive.google.com
session: 1.2. Physicians, Physician Practices, and Physician Payment:
1.2.1. Physician Practices
1.2.2. Physician Payment
learning_outcomes: T
materials: drive.google.com
session: 1.3. Hospitals, Other Provider Organizations, and Related Payment Systems
1.3.1. Hospitals overview
1.3.2. Hospital payment
1.3.3. Non-Hospital facilities
learning_outcomes: T
materials: drive.google.com
session: 1.3. Hospitals, Other Provider Organizations, and Related Payment Systems
1.3.4. Larger provider orgnizations
1.3.5. Pay for performance in provider payment
1.3.6. Providers and electronic records
learning_outcomes: T
materials: drive.google.com
session: 1.4. Intermediaries, Health Insurance Plans, and Health Care Financing:
1.4.1. Intermediaries overview
1.4.2. Intermediary approaches to influencing healthcare utilization and spending
learning_outcomes: T
materials: drive.google.com
session: 1.4. Intermediaries, Health Insurance Plans, and Health Care Financing:
1.4.3. Common health plan designs
1.4.4. Different intermediaries offering healthcare coverage
learning_outcomes: T
materials: drive.google.com
session: 1.5. Health Care Products and Prescription Drugs, and Quality Measurement and Improvement:
1.5.1. Healthcare product regulatiun: overview
1.5.2. Precription drug regulation and pricing
learning_outcomes: T
materials: drive.google.com
session: 1.5. Health Care Products and Prescription Drugs, and Quality Measurement and Improvement:
1.5.3. Quality measurement and improvement
learning_outcomes: T
materials: drive.google.com
session: 1.6. Ethics in Healthcare System
1.6.1. Overview of AI applications in delivery of health care services and ethical issues
1.6.2. Ethical frameworks for health care and for AI
learning_outcomes: T
materials: drive.google.com
session: 1.6. Ethics in Healthcare System
1.6.3. AI and incentives in health care delivery and payment structures
1.6.4. More examples of AI and incentives in health care delivery and payment structures
learning_outcomes: T
materials: drive.google.com
session: Review the introduction to healthcare
learning_outcomes: U
materials: drive.google.com
session: Assignment 1
materials: drive.google.com
session: Review Assignment 1
learning_outcomes: U
materials: drive.google.com
session: 2.1. Asking and answering questions via clinical data mining:
2.1.1. The data mining workflow
2.1.2. Types of research questions
learning_outcomes: T
materials: drive.google.com
session: 2.2. Data available from Healthcare systems:
2.2.1. The healthcare system
2.2.2. Healthcare data types
learning_outcomes: T
materials: drive.google.com
session: 2.2. Data available from Healthcare systems:
2.2.3. Sources of bias and errors
2.2.4. Healthcare data sources
learning_outcomes: T
materials: drive.google.com
session: 2.3. Representing time, and timing of events, for clinical data mining:
2.3.1. Healthcare happens over time
2.3.2. Representation of time
2.3.3. Data change over time
learning_outcomes: T
materials: drive.google.com
session: 2.3. Representing time, and timing of events, for clinical data mining:
2.3.3. Data change over time
learning_outcomes: T
materials: drive.google.com
session: 2.4. Creating analysis ready datasets from patient timelines
2.4.1. Creating features to analyze
2.4.2. Missing values
learning_outcomes: T
materials: drive.google.com
session: 2.4. Creating analysis ready datasets from patient timelines
2.4.3. Creating new features
2.4.4. Knowledge graphs
learning_outcomes: T
materials: drive.google.com
session: 2.5. Handling unstructured healthcare data: text, images, signals:
2.5.1. Unstructured data
2.5.2. Clinical text
learning_outcomes: T
materials: drive.google.com
session: 2.5. Handling unstructured healthcare data: text, images, signals:
2.5.3. Images
2.5.4. Signals
learning_outcomes: T
materials: drive.google.com
session: 2.6. Putting the pieces together: Electronic phenotyping
2.6.1. Electronic phenotyping
learning_outcomes: T
materials: drive.google.com
session: 2.6. Putting the pieces together: Electronic phenotyping
2.6.2. Two approaches to phenotyping
learning_outcomes: T
materials: drive.google.com
session: 2.7. Ethics in Clinical Data
2.7.1. Research Ethics and AI
2.7.2. The Belmont Report: A Framework for Research Ethics
2.7.3. Ethical Issues in Data sources for AI
learning_outcomes: T
materials: drive.google.com
session: 2.7. Ethics in Clinical Data
2.7.4. Secondary Uses of Data
2.7.5. Return of Results
2.7.6. AI and The Learning Health System
learning_outcomes: T
materials: drive.google.com
session: Review the introduction to clinical data
learning_outcomes: U
materials: drive.google.com
session: Progress Test 1
materials: drive.google.com
session: Review the Test 1
learning_outcomes: U
materials: drive.google.com
session: 3.1. Why machine learning in healthcare?
3.1.1. History of machine learning in healthcare
3.1.2. The magic of machine learning and the different approaches
learning_outcomes: T
materials: drive.google.com
session: 3.2. Concepts and Principles of machine learning in healthcare part 1
3.2.1. Machine Learning Terms, Definitions, and Jargon
3.2.3. How Machines Learn
learning_outcomes: T
materials: drive.google.com
session: 3.2. Concepts and Principles of machine learning in healthcare part 1
3.2.4. Supervised Machine Learning
3.2.5. Traditional machine learning
learning_outcomes: T
materials: drive.google.com
session: 3.3. Concepts and Principles of machine learning in healthcare part 2
3.3.1. Deep learning and neural networks
3.3.2. Important concepts in deep learning
learning_outcomes: T
materials: drive.google.com
session: 3.3. Concepts and Principles of machine learning in healthcare part 2
3.3.3. Types of neural networks and applications
3.3.4. Overview of common neural networks
learning_outcomes: T
materials: drive.google.com
session: 3.4. Evaluation and Metrics for machine learning in healthcare
3.4.1. Critical evaluation of models and strategies for healthcare applications
learning_outcomes: T
materials: drive.google.com
session: 3.4. Evaluation and Metrics for machine learning in healthcare
3.4.2. Important metrics for clinical machine learning
learning_outcomes: T
materials: drive.google.com
session: 3.5. Strategies and Challenges in Machine Learning in Healthcare
3.5.1. Challenges and strategies for clinical machine learning
3.5.2. Interpretability and performance of machine learning models in healthcare
learning_outcomes: T
materials: drive.google.com
session: 3.5. Strategies and Challenges in Machine Learning in Healthcare
3.5.3. Medical data for machine learning
learning_outcomes: T
materials: drive.google.com
session: 3.6. Best practices, teams, and launching your machine learning journey
3.6.1. Designing and evaluating clinical machine learning applications
learning_outcomes: T
materials: drive.google.com
session: 3.6. Best practices, teams, and launching your machine learning journey
3.6.2. Human factors in clinical machine learning - from job displacement to automation bias
3.6.3. Recommended Reading for Ethics
learning_outcomes: T
materials: drive.google.com
session: Review the fundamentals of machine learning for healthcare
learning_outcomes: U
materials: drive.google.com
session: Assignment 2
materials: drive.google.com
session: Review Assignment 2
learning_outcomes: U
materials: drive.google.com
session: 4.1. Al in Healthcare
4.1.1. Current Al Models in Healthcare
4.1.2. Growth of Alin Healthcare
4.1.3. How to Know if an Al model is Good
learning_outcomes: T
materials: drive.google.com
session: 4.2. Evaluations of Al in Healthcare
4.2.1. A Framework for Evaluation
4.2.2. Outcome: Action Pairing
learning_outcomes: T
materials: drive.google.com
session: 4.2. Evaluations of Al in Healthcare
4.2.3. Clinical Utility
4.2.4. Feasibility
learning_outcomes: T
materials: drive.google.com
session: 4.3. Al Deployment
4.3.1. Al Deployment
4.3.2. Design and Development
4.3.3. Evaluate and Validate
learning_outcomes: T
materials: drive.google.com
session: 4.3. Al Deployment
4.3.4. Product Validation
4.3.5. Difuse and Scale
4.3.6. Monitoring and Maintenance
4.3.7. Challenges
learning_outcomes: T
materials: drive.google.com
session: 4.4. Downstream Evaluations of Al in Healthcare: Bias and Fairness
4.4.1. Bias in Al Solutions
4.4.2. Types of Bias
learning_outcomes: T
materials: drive.google.com
session: 4.4. Downstream Evaluations of Al in Healthcare: Bias and Fairness
4.4.3. Algorithmic Fairness
4.4.4. Transparency
4.4.5. Downstream Evaluations
learning_outcomes: T
materials: drive.google.com
session: 4.5. The Regulatory Environment for Al in Healthcare
4.5.1. Overview
4.5.2. Components of Regulation
4.5.3. Clinical Evaluation Process
learning_outcomes: T
materials: drive.google.com
session: 4.5. The Regulatory Environment for Al in Healthcare
4.5.4. FDA Application
4.5.5. Product Approval
4.5.6. Global Environment
learning_outcomes: T
materials: drive.google.com
session: 4.6. Best Ethical Practices for Al in Health Care
4.6.1. Problem Formulation
4.6.2. Identifying Conflicts of Interest
4.6.3. Mitigating Conflicts of Interest
learning_outcomes: T
materials: drive.google.com
session: Review the evaluations of AI applications in healthcare
learning_outcomes: U
materials: drive.google.com
session: Progress Test 2
learning_outcomes: U
materials: drive.google.com
session: Review Progress Test 2
materials: drive.google.com
session: Assignment 3
learning_outcomes: U
materials: drive.google.com
session: Summary All Slides, Tests, and Assignments
learning_outcomes: U
materials: drive.google.com