University of Glasgow - Deep learning in Electronic Health Records - CDSS 2
- Offered byCoursera
Deep learning in Electronic Health Records - CDSS 2 at Coursera Overview
Duration | 39 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Deep learning in Electronic Health Records - CDSS 2 at Coursera Highlights
- Earn a Certificate upon completion from University of Glasgow
Deep learning in Electronic Health Records - CDSS 2 at Coursera Course details
- 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
Deep learning in Electronic Health Records - CDSS 2 at Coursera Curriculum
Artificial Intelligence and Multi-Layer Perceptron
Welcome Video - Deep Learning in Electronic Health Records
Deep Learning and Artificial Intelligence
Multi-Layer Perceptron
Training a Multi-Layer Perceptron
Optimization of a Multi-Layer Perceptron (Part 1)
Optimization of a Mutli-Layer Perceptrion (Part 2)
Preprocessing of ECG Signal
Artificial Intelligence
Deep Learning for Health Informatics
Practical Exercise: Pre-process ECG data for arrythmia detection
Practical Exercise: Split and resample ECG data
Practical Exercise: Classify beats using MLP and SVC models and the holdout beats validation protocol
Week 1 summary quiz
Convolutional and Recurrent Neural Networks.
Validation of Machine Learning Models
Convolutional Neural Networks
Recurrent Neural Networks
Evaluating Learning Algorithms: A
Practical Exercise: Classify beats using MLP and SVC models and the leave out patients validation protocol
Practical Exercise: Classify beats using a CNN and the beat holdout validation protocol
Practical Exercise: Classify beats using a CNN and the leave-out patients validation protocol
Practical Exercise: Classify beats using an LSTM and the beat holdout validation protocol
Practical Exercise: Classify beats using an LSTM and the leave-out patients validation protocol
End of week 2 quiz
Preprocessing and imputation of MIMIC III data
Benchmark Deep Learning Models with EHR - Part 1
Benchmark Deep Learning Models with EHR - Part 2
Imputation Strategies
Deep Learning Imputation Strategies
A Data Extraction and Representation Pipeline
Practical Exercise: Patients and time-series data extraction of MIMIC-III
Creation of Benchmark data for DNN
Practical Exercise: Pre-processing of MIMIC-III dataset
Practical Exercise: One-hot encoding and in-hospital mortality prediction
Practical Exercise: in-hospital mortality prediction using one-hot encoding and undersampling
Practical Exercise: Mean vs Joint modelling imputation
Imputation based on moments
End of week 3 quiz
EHR Encodings for machine learning models
Categorical and Continuous Variables
Bayesian Target Encoding
Encodings Inspired from NLP
Other Types of Embeddings
Practical Exercise: mean target encoding
Practical exercise: leave one out encoding
Representation Learning for Electronic Health Records
Practical Exercise: encoding using an autoencoder
Similarity Encodings
End of week 4 quiz
End of course summative quiz
Deep learning in Electronic Health Records - CDSS 2 at Coursera Admission Process
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