University of Glasgow
Deep learning in Electronic Health Records - CDSS 2
University of Glasgow

Deep learning in Electronic Health Records - CDSS 2

Fani Deligianni

Instructor: Fani Deligianni

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

31 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

31 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the Informed Clinical Decision Making using Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

7 videos5 readings1 quiz1 discussion prompt4 ungraded labs

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.

What's included

3 videos6 readings1 quiz1 discussion prompt5 ungraded labs

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.

What's included

4 videos8 readings1 quiz1 discussion prompt5 ungraded labs

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.

What's included

4 videos5 readings2 quizzes1 discussion prompt4 ungraded labs

Instructor

Fani Deligianni
University of Glasgow
5 Courses3,867 learners

Offered by

Recommended if you're interested in Machine Learning

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