Capstone Assignment - CDSS 5

Capstone Assignment - CDSS 5

This course is part of Informed Clinical Decision Making using Deep Learning Specialization

Instructor: Fani Deligianni

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

3 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

3 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Informed Clinical Decision Making using Deep Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

This course is a capstone assignment requiring you to apply the knowledge and skill you have learnt throughout the specialization. In this course you will choose one of the areas and complete the assignment to pass.

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

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.