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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Spécialisation Informed Clinical Decision Making using Deep LearningUniversity of Glasgow À propos de ce cours
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Certificat partageable
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Cours 2 sur 5 dans le
Niveau intermédiaire
Python programming and experience with scientific packages such as numpy, scipy and matplotlib.
Approx. 32 heures pour terminer
Anglais
Sous-titres : Anglais
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
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 5 dans le
Niveau intermédiaire
Python programming and experience with scientific packages such as numpy, scipy and matplotlib.
Approx. 32 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
University of Glasgow
The University of Glasgow has been changing the world since 1451. It is a world top 100 university (THE, QS) with one of the largest research bases in the UK.
Programme de cours : ce que vous apprendrez dans ce cours
6 heures pour terminer
Artificial Intelligence and Multi-Layer Perceptron
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.
6 heures pour terminer
7 vidéos (Total 62 min), 5 lectures, 1 quiz
8 heures pour terminer
Convolutional and Recurrent Neural Networks.
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.
8 heures pour terminer
3 vidéos (Total 64 min), 6 lectures, 1 quiz
10 heures pour terminer
Preprocessing and imputation of MIMIC III data
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.
10 heures pour terminer
4 vidéos (Total 65 min), 8 lectures, 1 quiz
8 heures pour terminer
EHR Encodings for machine learning models
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.
8 heures pour terminer
4 vidéos (Total 52 min), 5 lectures, 2 quiz
À propos du Spécialisation Informed Clinical Decision Making using Deep Learning
This specialisation is for learners with experience in programming that are interested in expanding their skills in applying deep learning in Electronic Health Records and with a focus on how to translate their models into Clinical Decision Support Systems.
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