Deep Learning in Electronic Health Records

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

• Train deep learning models for classification
• Validate and compare machine learning algorithms
• Preprocess electronic health records as time-series data
• Apply imputation and encoding strategies

• Basic familiarity with topic and terminology
• Willingness to engage in applied exercises or case-based work