AI for Medicine (AIM301m)
Vietnamese title: Ứng dụng TTNT cho y học
Credits: 3
Prerequisites: PFP191

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:
Pre-process data and set up a pre-trained neural network.
Evaluate models by accuracy, specificity, predictive value, confidence intervals, ROC curve, and F1 score.
Diagnose diseases from x-rays and 3D MRI brain images.
Walk through examples of prognostic tasks.
Predict patient survival rates more accurately using tree-based models.
Navigate practical challenges in medicine, like missing data.
Estimate treatment effects on patients using data from randomized trials.
Explore methods to interpret diagnostic and prognostic models.
Automate the task of labeling medical datasets using natural language processing.

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: Spec: AI for Medicine Specialization
author: Coursera
publisher: Coursera
note: https://www.coursera.org/specializations/ai-for-medicine#courses
description: MOOC 1: AI for Medical Diagnosis
author: Coursera
publisher: Coursera
note: https://www.coursera.org/learn/ai-for-medical-diagnosis?specialization=ai-for-medicine
description: MOOC 2: AI for Medical Prognosis
author: Coursera
publisher: Coursera
note: https://www.coursera.org/learn/ai-for-medical-prognosis?specialization=ai-for-medicine
description: MOOC 3: AI For Medical Treatment
author: Coursera
publisher: Coursera
note: https://www.coursera.org/learn/ai-for-medical-treatment?specialization=ai-for-medicine
description: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
author: Aurélien Géron
publisher: O'Reilly Media
published_date: 5/9/2017 12:00:00 AM
edition: 1st edition
isbn: 978-1491962299
note: http://ds.libol.fpt.edu.vn/
description: Healthcare IT Support Specialization
author: Coursera
publisher: Coursera
note: https://www.coursera.org/specializations/healthcare-it
description: AI For Everyone
author: Coursera
publisher: Coursera
note: https://www.coursera.org/learn/ai-for-everyone

Assessments:
category: Assignment 1
type: on-going
weight: 5.0
duration: 60'
question_type: CLO1
no_question: Personal task (programming) (50%) and Group presentation (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 1-4
note: in class, by group and personal presentations
category: Assignment 2
type: on-going
weight: 5.0
duration: 60'
question_type: CLO2, CLO3
no_question: Personal task (programming) (50%) and Group presentation (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 5-8
note: in class, by group and personal presentations
category: Assignment 3
type: on-going
weight: 5.0
duration: 60'
question_type: CLO4, CLO5
no_question: Personal task (programming) (50%) and Group presentation (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 9-15
note: in class, by group and personal presentations
category: Assignment 4
type: on-going
weight: 5.0
duration: 60'
question_type: CLO6
no_question: Personal task (programming) (50%) and Group presentation (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 16-20
note: in class, by group and personal presentations
category: Assignment 5
type: on-going
weight: 5.0
duration: 60'
question_type: CLO7
no_question: Personal task (programming) (50%) and Group presentation (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 21-23
note: in class, by group and personal presentations
category: Assignment 6
type: on-going
weight: 5.0
duration: 60'
question_type: CLO8 - CLO9
no_question: Personal task (programming) (50%) and Group presentation (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 24-26
note: in class, by group and personal presentations
category: Progress test 1
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO1, CLO2, CLO3
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 25
grading_guide: concepts (AI for medical diagnosis), slides 1.1-1.3
note: in class, by instructor
category: Progress test 2
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO4, CLO5, CLO6
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 25
grading_guide: concepts (AI for medical prognosis), slides 2.1-2.4
note: in class, by instructor
category: Progress test 3
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO7, CLO8, CLO9
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 25
grading_guide: concepts (AI for medical treatment), slides 3.1-3.3
note: in class, by instructor
category: Final exam
type: Final exam
weight: 40.0
duration: 60'
question_type: CLO1 - CLO9
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 50
grading_guide: concepts, algorithms;
all studied slides and labs
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 the way to log on the system to submit the assignments and class forum with enroll key.
learning_outcomes: IT
materials: mat1
session: 1. AI for Medical Diagnosis
1.1. Disease Detection with Computer Vision
Applications of Computer Vision to Medical Diagnosis
Handling Class Imbalance and Small Training Sets
Checking your Model Performance
learning_outcomes: T
session: Lab 1 - Data Exploration & Image Pre-Processing
learning_outcomes: T
session: Lab 2 - Counting labels and weighted loss function
learning_outcomes: T
session: Lab 3 - Densenet
learning_outcomes: T
session: Lab 4 - Patient Overlap - Practice
learning_outcomes: T
session: Assignment 1: Chest X-Ray Medical Diagnosis with Deep Learning
learning_outcomes: U
session: Review Assignment 1
learning_outcomes: U
session: 1. AI for Medical Diagnosis
1.2. Evaluating Models
Key Evaluation Metrics
Threshold and Evaluation Metrics
Interpreting Confidence Intervals Correctly
learning_outcomes: T
session: Lab 5 - ROC Curve and Threshold
learning_outcomes: T
session: 1. AI for Medical Diagnosis
1.3. Image Segmentation on MRI Images
Exploring MRI Data
Image Segmentation
Practical Considerations
learning_outcomes: T
session: Lab 6 - Explore MRI data
learning_outcomes: T
session: Lab 7 - Get a sub section
learning_outcomes: T
session: Lab 8 - Implement U-Net
learning_outcomes: T
session: Assignment 2: Brain Tumor Auto-Segmentation for Magnetic Resonance Imaging (MRI)
learning_outcomes: U
session: Review Assignment 2
learning_outcomes: U
session: Summary AI for Medical Diagnosis
learning_outcomes: T
session: Progress Test 1
learning_outcomes: U
session: Review Progress Test 1
learning_outcomes: U
session: 2. AI for Medical Prognosis
2.1. Linear Prognostic Models
What is the Risk of Getting a Disease?
Prognostic Models in Medical Practice
Representing Feature Interactions
Evaluating Prognostic Models
learning_outcomes: T
session: Lab 9 - Create a linear model
learning_outcomes: T
session: Lab 10 - Risk Scores, Pandas and Numpy
learning_outcomes: T
session: Lab 11 - Combine Features
learning_outcomes: T
session: Lab 12 - Concordance index
learning_outcomes: T
session: 2. AI for Medical Prognosis
2.2. Prognosis with Tree-based Models
Tree-based Models
Identifying Missing Data
Using Imputation to Handle Missing Data
learning_outcomes: T
session: Lab 13 - Decision trees
learning_outcomes: T
session: Lab 14 - Missing data and applying a mask
learning_outcomes: T
session: Lab 15 - Imputation
learning_outcomes: T
session: Assignment 3: Risk Models Using Tree-based Models
learning_outcomes: U
session: Review Assignment 3
learning_outcomes: U
session: 2. AI for Medical Prognosis
2.3. Survival Models and Time
Survival Estimates
Time to Event Data
Estimate Survival with Censored Data
learning_outcomes: T
session: Lab 16 - Counting patients
learning_outcomes: T
session: Lab 17 - Kaplan Meier
learning_outcomes: T
session: 2. AI for Medical Prognosis
2.4. Build a Risk Model Using Linear and Tree-based Models
Survival and Hazard Functions
Customizing Risk Models to Individual Patients
Non-linear Risk Models with Survival Trees
Evaluate Survival Models
learning_outcomes: T
session: Lab 18 - Categorical variables
learning_outcomes: T
session: Lab 19 - Hazard function
learning_outcomes: T
session: Lab 20 - Permissible pairs
learning_outcomes: T
session: Assignment 4: Cox proportional hazards and random survival forests
learning_outcomes: U
session: Review Assignment 4
learning_outcomes: U
session: Summary AI for Medical Prognosis
learning_outcomes: T
session: Progress Test 2
learning_outcomes: U
session: Review Progress Test 2
learning_outcomes: U
session: 3. AI For Medical Treatment
3.1. Treatment Effect Estimation
Introduction
Randomized Control Trials
Average Treatment Effect
Individualized Treatment Effect
learning_outcomes: T
session: Lab 21 - Pandas operations on Medical Data
learning_outcomes: T
session: Lab 22 - Model Training Tuning Basics with Sklearn
learning_outcomes: T
session: Lab 23 - Logistic Regression Model Interpretation
learning_outcomes: T
session: Assignment 5: Treatment Effect Estimation
learning_outcomes: U
session: Review Assignment 5
learning_outcomes: U
session: 3. AI For Medical Treatment
3.2. Medical Question Answering
Question Answering
Automatic Labeling
Evaluate Automatic Labeling
learning_outcomes: T
session: Lab 24 - Cleaning Text
learning_outcomes: T
session: Lab 25 - BioC format and the NegBio Library
learning_outcomes: T
session: Lab 26 - Preparing Input for Text Classification
learning_outcomes: T
session: Assignment 6: Natural Language Entity Extraction
learning_outcomes: U
session: Review Assignment 6
learning_outcomes: U
session: 3. AI For Medical Treatment
3.3. ML Interpretation
Feature Importance
Individual Feature Importance
Interpreting Deep Learning Models
learning_outcomes: T
session: Lab 27 - Permutation method
learning_outcomes: T
session: Lab 28 - GradCam Part 1 & 2
learning_outcomes: T
session: Summary AI For Medical Treatment
learning_outcomes: T
session: Progress Test 3
learning_outcomes: U
session: Review Progress Test 3
learning_outcomes: U