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.
Ce cours fait partie de la Spécialisation Informed Clinical Decision Making using Deep Learning
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À propos de ce cours
Python programming and experience with scientific packages such as numpy, scipy and matplotlib.
Ce que vous allez apprendre
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
Compétences que vous acquerrez
- preprocessing of EHR and imputation
- Convolutional Neural Network
- deep learning and validation
- Recurrent Neural Network
- data encodings and autoencoders
Python programming and experience with scientific packages such as numpy, scipy and matplotlib.
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Programme de cours : ce que vous apprendrez dans ce cours
Artificial Intelligence and Multi-Layer Perceptron
Convolutional and Recurrent Neural Networks.
Preprocessing and imputation of MIMIC III data
EHR Encodings for machine learning models
À propos du Spécialisation Informed Clinical Decision Making using Deep Learning

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