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Deep Learning in Electronic Health Records

Deep Learning in Electronic Health Records

本课程是 Informed Clinical Decision Making using Deep Learning 专项课程的一部分

位教师：Fani Deligianni

2,095 人已注册

包含在中

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4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Train deep learning architectures such as Multi-layer perceptron, Convolutional Neural Networks and Recurrent Neural Networks for classification.
Validate and compare different machine learning algorithms.
Preprocess Electronic Health Records and represent them as time-series data.
Imputation strategies and data encodings.

您将获得的技能

您将学习的工具

Autoencoders

要了解的详细信息

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作业

5 项作业

授课语言：英语（English）

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积累特定领域的专业知识

本课程是 Informed Clinical Decision Making using Deep Learning 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有4个模块

Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG. Applying this methods in Electronic Health Records is challenging due to the missing values and the heterogeneity in EHR, which include both continuous, ordinal and categorical variables. Subsequently, explore imputation techniques and different encoding strategies to address these issues. Apply these approaches to formulate clinical prediction benchmarks derived from information available in MIMIC-III database.

单元详情

This week includes an overview of deep learning history and popular deep learning platforms. Subsequently, Multi-Layer Perceptron (MLP) Networks are discussed along with common activation functions, loss functions and optimisation algorithms. Finally, the practical exercises will allow to optimise and evaluate MLP in ECG classification.

涵盖的内容

7个视频5篇阅读材料1个作业1个讨论话题4个非评分实验室

7个视频总计62分钟

Welcome Video - Deep Learning in Electronic Health Records1分钟
Deep Learning and Artificial Intelligence17分钟
Multi-Layer Perceptron9分钟
Training a Multi-Layer Perceptron8分钟
Optimization of a Multi-Layer Perceptron (Part 1)9分钟
Optimization of a Mutli-Layer Perceptrion (Part 2)11分钟
Preprocessing of ECG Signal7分钟

5篇阅读材料总计240分钟

Artificial Intelligence30分钟
Deep Learning for Health Informatics30分钟
Practical Exercise: Pre-process ECG data for arrythmia detection60分钟
Practical Exercise: Split and resample ECG data60分钟
Practical Exercise: Classify beats using MLP and SVC models and the holdout beats validation protocol60分钟

1个作业总计30分钟

Week 1 summary quiz30分钟

1个讨论话题总计10分钟

Week 1 - Your experience10分钟

4个非评分实验室总计40分钟

Pre-process ECG data for arrythmia detection10分钟
Split and resample ECG data10分钟
Classification of beats using MLP and SVC models and beat holdout method10分钟
Classification of beats using MLP and SVC models and leave out patients method10分钟

Convolutional Neural Networks (CNNs) revolutionised the way we process images and they contributed significantly in deep learning success. This week we are going to discuss what advantages CNNs offer over MLP and we will implement CNNs for time-series classifications. Subsequently, we are going to present Recurrent Neural Networks (RNNs). In particular, we are going to discuss Long-Short Term Memory Networks and Gated Recurrent Unit Networks. Practical exercises will allow to design and train all these types of networks in ECG classification. The importance of training, validation and testing datasets will be emphasised for avoiding overfitting and model evaluation.

涵盖的内容

3个视频6篇阅读材料1个作业1个讨论话题5个非评分实验室

3个视频总计64分钟

Validation of Machine Learning Models26分钟
Convolutional Neural Networks19分钟
Recurrent Neural Networks19分钟

6篇阅读材料总计315分钟

Evaluating Learning Algorithms: A60分钟
Practical Exercise: Classify beats using MLP and SVC models and the leave out patients validation protocol60分钟
Practical Exercise: Classify beats using a CNN and the beat holdout validation protocol60分钟
Practical Exercise: Classify beats using a CNN and the leave-out patients validation protocol30分钟
Practical Exercise: Classify beats using an LSTM and the beat holdout validation protocol60分钟
Practical Exercise: Classify beats using an LSTM and the leave-out patients validation protocol45分钟

1个作业总计30分钟

End of week 2 quiz30分钟

1个讨论话题总计10分钟

Week 2 - Your experience10分钟

5个非评分实验室总计50分钟

Classification of beats using a CNN model and the holdout beats validation protocol10分钟
Classification of beats using a CNN model and the leave out patients validation protocol10分钟
Classification of beats using an LSTM model and the holdout beats validation protocol10分钟
Classification of beats using an LSTM model and the leave out patients validation protocol10分钟
Dimensionality reduction techniques to visualize ECG data10分钟

Developing benchmark datasets for DNNs based on MIMIC-III database involves several steps that include cohort selection, unit conversion, outlier removal and aggregation of data within time windows. The later step allows to represent EHR as time-series data but it is also susceptible to missing data. For this reason imputation strategies both based on traditional and deep learning techniques are presented. The learner will have the opportunity to preprocess EHR and train deep learning models in predicting in-hospital mortality.

涵盖的内容

4个视频8篇阅读材料1个作业1个讨论话题5个非评分实验室

4个视频总计65分钟

Benchmark Deep Learning Models with EHR - Part 115分钟
Benchmark Deep Learning Models with EHR - Part 217分钟
Imputation Strategies13分钟
Deep Learning Imputation Strategies20分钟

8篇阅读材料总计440分钟

A Data Extraction and Representation Pipeline60分钟
Practical Exercise: Patients and time-series data extraction of MIMIC-III60分钟
Creation of Benchmark data for DNN50分钟
Practical Exercise: Pre-processing of MIMIC-III dataset60分钟
Practical Exercise: One-hot encoding and in-hospital mortality prediction60分钟
Practical Exercise: in-hospital mortality prediction using one-hot encoding and undersampling60分钟
Practical Exercise: Mean vs Joint modelling imputation60分钟
Imputation based on moments30分钟

1个作业总计30分钟

End of week 3 quiz30分钟

1个讨论话题总计10分钟

Week 3 - Your experience10分钟

5个非评分实验室总计50分钟

Study cohort selection and variable extraction10分钟
Imputation methods for in-hospital mortality prediction10分钟
In-hospital mortality prediction using one-hot encoding10分钟
In-hospital mortality prediction using one-hot encoding and undersampling10分钟
Pre-processing10分钟

EHRs include categorical, ordinal and continuous variables. Appropriate data representation is important and encodings affect prediction performance. This week includes several different strategies to encode the data such as target encodings, deep learning encodings and similarity encodings. In particular, autoencoders which is a deep learning architecture to represent data in lower dimensional space will be demonstrated and applied in in-hospital mortality prediction.