Capstone Assignment
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Capstone Assignment
This course is part of Informed Clinical Decision Making using Deep Learning Specialization
Instructor: Fani Deligianni
Included with
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What you'll learn
Apply clinical data mining, deep learning, and explainable AI to a real-world prediction task using MIMIC-III data.
Implement and compare global and local explainability methods to interpret logistic regression and LSTM models.
Evaluate the clinical relevance and trustworthiness of model explanations for informed clinical decision support.
Skills you'll gain
- Feature Engineering
- Applied Machine Learning
- Health Informatics
- Model Evaluation
- Model Training
- Machine Learning
- Decision Intelligence
- Predictive Modeling
- Predictive Analytics
- Data Preprocessing
- Logistic Regression
- Recurrent Neural Networks (RNNs)
- Health Information Management
- Deep Learning
- Clinical Informatics
- Clinical Data Management
Details to know
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There are 3 modules in this course
This capstone course gives you the opportunity to bring everything you have learned in the Informed Clinical Decision Making using Deep Learning Specialization together in one hands-on, practical project. You will work with real-world critical care data from the MIMIC-III database and tackle a clinically meaningful prediction task from start to finish.
You will choose one of three advanced projects focused on explainable artificial intelligence in healthcare: permutation feature importance, LIME, or Grad-CAM. Each project guides you through querying and preparing electronic health record data, building predictive models such as logistic regression or LSTM networks, and interpreting model predictions using state-of-the-art explainability techniques. The focus is not only on model performance, but on understanding and communicating why a model makes its predictions. By completing this capstone, you will gain practical experience translating deep learning models into insights that support trustworthy and transparent Clinical Decision Support Systems. This course is ideal for learners who want to demonstrate applied skills, build confidence working with clinical data, and showcase their ability to combine technical expertise with clinical reasoning.
This is an advanced exercise/lesson that combines knowledge from the three earlier modules: 1) 'Data mining of Clinical Databases' to query the MIMIC database, 2) 'Deep learning in Electronic Health Records' to pre-process EHR and build deep learning models and 3) 'Explainable deep learning models for healthcare' to explain the models decision. In particular, permutation feature importance is implemented and applied on MIMIC-III extracted datasets. The technique is applied both on logistic regression and on an LSTM model. The explanations derived are global explanations of the model.
What's included
3 readings1 assignment
3 readings•Total 30 minutes
- IMPORTANT COURSE INFORMATION•10 minutes
- Interpretability of in-hospital mortality classification using Permutation Feature Importance and logistic regression•10 minutes
- Interpretability of in-hospital mortality classification using Permutation Feature Importance and an LSTM model•10 minutes
1 assignment•Total 30 minutes
- Permutation feature importance on the MIMIC critical care database•30 minutes
This is an advanced exercise/lesson that combines knowledge from the three earlier modules: 1) 'Data mining of Clinical Databases' to query the MIMIC database, 2) 'Deep learning in Electronic Health Records' to pre-process EHR and build deep learning models and 3) 'Explainable deep learning models for healthcare' to explain the models decision. In particular, LIME is applied on MIMIC-III extracted datasets. The technique is applied on both logistic regression and an LSTM model . The explanations derived are local explanations of the model.
What's included
2 readings1 assignment
2 readings•Total 20 minutes
- Interpretability of in-hospital mortality classification using LIME and logistic regression•10 minutes
- Interpretability of in-hospital mortality classification using LIME and an LSTM•10 minutes
1 assignment•Total 30 minutes
- LIME on the MIMIC critical care database•30 minutes
This is an advanced exercise/lesson that combines knowledge from the three earlier modules: 1) 'Data mining of Clinical Databases' to query the MIMIC database, 2) 'Deep learning in Electronic Health Records' to pre-process EHR and build deep learning models and 3) 'Explainable deep learning models for healthcare' to explain the models decision. In particular, GradCam is implemented and applied on an LSTM model that predicts mortality based on MIMIC-III extracted datasets. The explanations derived are local explanations of the model.
What's included
1 reading1 assignment
1 reading•Total 10 minutes
- Interpretability of in-hospital mortality classification using Gradient-weighted Class Activation Mapping and an LSTM model •10 minutes
1 assignment•Total 30 minutes
- Grad-CAM on the MIMIC critical care database•30 minutes
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