Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

University of Glasgow

Capstone Assignment - CDSS 5

University of Glasgow via Coursera

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
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.

Syllabus

  • Permutation feature importance on the MIMIC critical care database
    • 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.
  • LIME on the MIMIC critical care database
    • 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.
  • Grad-CAM on the MIMIC critical care database
    • 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.

Taught by

Fani Deligianni

Reviews

Start your review of Capstone Assignment - CDSS 5

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.