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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Assessments

3 assignments

Taught in English

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

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There are 3 modules in this course

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 readingsTotal 30 minutes

IMPORTANT COURSE INFORMATION10 minutes
Interpretability of in-hospital mortality classification using Permutation Feature Importance and logistic regression10 minutes
Interpretability of in-hospital mortality classification using Permutation Feature Importance and an LSTM model10 minutes

1 assignmentTotal 30 minutes

Permutation feature importance on the MIMIC critical care database30 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.