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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/clin-decision-deep-learning
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课程名称：使用深度学习进行知情临床决策制定 课程概述： 本课程旨在帮助学员掌握如何提取和预处理复杂的临床数据库中的数据，应用深度学习于电子健康记录（EHR），以及构建可解释、公平且保护隐私的临床决策支持系统（CDSS）。 学习内容包括： - 从复杂的临床数据库中提取和预处理数据 - 在电子健康记录中应用深度学习 - 对电子健康记录进行插补及数据编码 - 维护临床决策支持系统的可解释性、公平性及隐私保护 掌握的技能： - 深度学习 - 机器学习 - 可解释机器学习 - 电子健康记录处理 - 临床决策支持系统 - 国际疾病分类（ICD） - 临床数据库挖掘 - 描述性统计 - EHR伦理学 - EHR的预处理及插补 - 卷积神经网络（CNN） 课程结构： 本专业化课程适合有编程经验的学习者，重点在于如何将深度学习模型转化为临床决策支持系统。主要探讨的领域包括临床数据库的数据挖掘、电子健康记录中的深度学习、可解释的深度学习模型以及临床决策支持系统的偏见、公平性和隐私问题。 实践学习项目： 学习者将有机会选择并进行基于MIMIC-III提取数据集的练习，结合以下知识： - 临床数据库数据挖掘 - 在电子健康记录中应用深度学习，进行EHR预处理与模型构建 - 为医疗保健中的可解释深度学习模型解释模型决策 可选项目： 1. 在MIMIC重症护理数据库上使用置换特征重要性。 2. 在MIMIC重症护理数据库上应用LIME技术。 3. 在MIMIC重症护理数据库上使用Grad-CAM方法。 特点： - 完成后可获得证书 - 全部在线课程，灵活安排时间 - 适合具备基本SQL和Python知识的本科和硕士最后一年学生 - 学习时间大约为5个月，每周建议学习5小时 - 提供英文在线学习及字幕支持 若您想了解更多信息，可访问课程链接：[使用深度学习进行知情临床决策制定](https://www.coursera.org/learn/cdss1)
Course Link: https://www.coursera.org/learn/cdss1
Name:Data mining of Clinical Databases - CDSS 1
Description:Offered by University of Glasgow . This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) ... Enroll for free.
Course Link: https://www.coursera.org/learn/cdss2
Name:Deep learning in Electronic Health Records - CDSS 2
Description:Offered by University of Glasgow . Overview of the main principles of Deep Learning along with common architectures. Formulate the problem ... Enroll for free.
Course Link: https://www.coursera.org/learn/cdss3
Name:Explainable deep learning models for healthcare - CDSS 3
Description:Offered by University of Glasgow . This course will introduce the concepts of interpretability and explainability in machine learning ... Enroll for free.
Course Link: https://www.coursera.org/learn/cdss4
Name:Clinical Decision Support Systems - CDSS 4
Description:Offered by University of Glasgow . Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external ... Enroll for free.
Course Link: https://www.coursera.org/learn/cdss5-capstoneassignment
Name:Capstone Assignment - CDSS 5
Description:Offered by University of Glasgow . This course is a capstone assignment requiring you to apply the knowledge and skill you have learnt ... Enroll for free.
What you will learn
Extract and preprocess data from complex clinical databases
Apply deep learning in Electronic Health Records
Imputation of Electronic Health Records and data encodings
Explainable, fair and privacy-preserved Clinical Decision Support Systems
Skills you will gain
Deep Learning
Machine Learning
Explainable Machine Learning
processing electronic health records
clinical decision support systems
International Classification of Diseases
mining clinical databases
Descriptive Statistics
Electronic Health Records
Ethics in EHR
preprocessing of EHR and imputation
Convolutional Neural Network
About this Specialization
This specialisation is for learners with experience in programming that are interested in expanding their skills in applying deep learning in Electronic Health Records and with a focus on how to translate their models into Clinical Decision Support Systems.
The main areas that would explore are:
Data mining of Clinical Databases: Ethics, MIMIC III database, International Classification of Disease System and definition of common clinical outcomes. Deep learning in Electronic Health Records: From descriptive analytics to predictive analytics Explainable deep learning models for healthcare applications: What it is and why it is needed Clinical Decision Support Systems: Generalisation, bias, ‘fairness’, clinical usefulness and privacy of artificial intelligence algorithms.
Applied Learning Project
Learners have the opportunity to choose and undertake an exercise based on MIMIC-III extracted datasets that combines knowledge from:
Data mining of Clinical Databases
to query the MIMIC database
Deep learning in Electronic Health Records
to pre-process EHR and build deep learning models
Explainable deep learning models for healthcare
to explain the models decision
Learners can choose from:
1. Permutation feature importance on the MIMIC critical care database
The technique is applied both on logistic regression and on an LSTM model. The explanations derived are global explanations of the model.
2. LIME on the MIMIC critical care database
The technique is applied on both logistic regression and an LSTM model. The explanations derived are local explanations of the model.
3. Grad-CAM on the MIMIC critical care database
GradCam is implemented and applied on an LSTM model that predicts mortality. The explanations derived are local explanations of the model.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Last year undergraduate or master students of computing science or engineering. Basic knowledge on SQL queries and python is required.
Hours to complete
Approximately 5 months to complete
Suggested pace of 5 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Last year undergraduate or master students of computing science or engineering. Basic knowledge on SQL queries and python is required.
Hours to complete
Approximately 5 months to complete
Suggested pace of 5 hours/week
Available languages
English
Subtitles: English
