Deep Learning in Electronic Health Records

About this course

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.

What you will 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.