Capstone Assignment - CDSS 5

所在平台: Coursera

课程主页: https://www.coursera.org/learn/cdss5-capstoneassignment

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课程简介

课程名称：期末作业 - CDSS 5 课程概述： 本课程是一个期末作业，要求学生运用在整个专业学习中所获得的知识和技能。学生将在课程中选择一个领域并完成作业以通过评估。 课程大纲： 第一部分： 标题：在MIMIC重症监护数据库上的置换特征重要性 描述：这是一个高级课程，结合了前面三个模块的知识：1）“临床数据库的数据挖掘”以查询MIMIC数据库，2）“电子健康记录中的深度学习”以预处理电子健康记录并构建深度学习模型，3）“面向医疗保健的可解释深度学习模型”以解释模型的决策。具体来说，置换特征重要性在从MIMIC-III提取的数据集上实施和应用。该技术同时应用于逻辑回归和LSTM模型，所获得的解释为模型的全局解释。 第二部分： 标题：在MIMIC重症监护数据库上的LIME 描述：这是一个高级课程，结合了前面三个模块的知识：1）“临床数据库的数据挖掘”以查询MIMIC数据库，2）“电子健康记录中的深度学习”以预处理电子健康记录并构建深度学习模型，3）“面向医疗保健的可解释深度学习模型”以解释模型的决策。具体来说，LIME被应用于从MIMIC-III提取的数据集。该技术同时应用于逻辑回归和LSTM模型，所获得的解释为模型的局部解释。 第三部分： 标题：在MIMIC重症监护数据库上的Grad-CAM 描述：这是一个高级课程，结合了前面三个模块的知识：1）“临床数据库的数据挖掘”以查询MIMIC数据库，2）“电子健康记录中的深度学习”以预处理电子健康记录并构建深度学习模型，3）“面向医疗保健的可解释深度学习模型”以解释模型的决策。具体来说，GradCAM被实施并应用于一个基于MIMIC-III提取数据集预测死亡率的LSTM模型，所获得的解释为模型的局部解释。

课程大纲

Part: 1

Title:Permutation feature importance on the MIMIC critical care database

Description: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.

Part: 2

Title:LIME on the MIMIC critical care database

Description: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.

Part: 3

Title:Grad-CAM on the MIMIC critical care database

Description: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.

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