Este curso forma parte de Programa especializado: Informed Clinical Decision Making using Deep Learning

Fani Deligianni

Ofrecido Por

Programa especializado: Informed Clinical Decision Making using Deep LearningUniversity of Glasgow

Acerca de este Curso

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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.

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Curso 2 de 5 en

Programa especializado: Informed Clinical Decision Making using Deep Learning

Nivel intermedio

Python programming and experience with scientific packages such as numpy, scipy and matplotlib.

Aprox. 31 horas para completar

Inglés (English)

Qué aprenderás

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

Habilidades que obtendrás

preprocessing of EHR and imputation
Convolutional Neural Network
deep learning and validation
Recurrent Neural Network
data encodings and autoencoders

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 5 en

Programa especializado: Informed Clinical Decision Making using Deep Learning

Nivel intermedio

Python programming and experience with scientific packages such as numpy, scipy and matplotlib.

Aprox. 31 horas para completar

Inglés (English)

Instructor

Fani Deligianni

School of Computing Science

1884 alumnos

5 cursos

Ofrecido por

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.

We are a member of the prestigious Russell Group of leading UK Universities with annual research income of more than £179m. The University’s #TeamUofG community is truly international with over 8000 staff and 28,0000 students from more than 140 countries. A 2019 Time Out survey placed Glasgow in the top ten cities in the world. Ranked between Berlin and Paris, Glasgow was voted number one for both friendliness and affordability. Right now our dedicated community of staff, students and alumni is working to address the challenges of Covid-19 and understand how we can make life safer for everyone.

Programa - Qué aprenderás en este curso

Semana1

Semana 1

6 horas para completar

Artificial Intelligence and Multi-Layer Perceptron

6 horas para completar

7 videos (Total 62 minutos), 5 lecturas, 1 cuestionario

7 videos

Welcome Video - Deep Learning in Electronic Health Records56sDeep Learning and Artificial Intelligence17mMulti-Layer Perceptron9mTraining a Multi-Layer Perceptron8mOptimization of a Multi-Layer Perceptron (Part 1)9mOptimization of a Mutli-Layer Perceptrion (Part 2)10mPreprocessing of ECG Signal6m

5 lecturas

Artificial Intelligence30mDeep Learning for Health Informatics30mPractical Exercise: Pre-process ECG data for arrythmia detection1hPractical Exercise: Split and resample ECG data1hPractical Exercise: Classify beats using MLP and SVC models and the holdout beats validation protocol1h

1 ejercicio de práctica

Week 1 summary quiz30m

Semana2

Semana 2

8 horas para completar

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 horas para completar

3 videos (Total 64 minutos), 6 lecturas, 1 cuestionario

3 videos

Validation of Machine Learning Models25mConvolutional Neural Networks19mRecurrent Neural Networks19m

6 lecturas

Evaluating Learning Algorithms: A1hPractical Exercise: Classify beats using MLP and SVC models and the leave out patients validation protocol1hPractical Exercise: Classify beats using a CNN and the beat holdout validation protocol1hPractical Exercise: Classify beats using a CNN and the leave-out patients validation protocol30mPractical Exercise: Classify beats using an LSTM and the beat holdout validation protocol1hPractical Exercise: Classify beats using an LSTM and the leave-out patients validation protocol45m

1 ejercicio de práctica

End of week 2 quiz30m

Semana3

Semana 3

10 horas para completar

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 horas para completar

4 videos (Total 65 minutos), 8 lecturas, 1 cuestionario

4 videos

Benchmark Deep Learning Models with EHR - Part 114mBenchmark Deep Learning Models with EHR - Part 217mImputation Strategies13mDeep Learning Imputation Strategies19m

8 lecturas

A Data Extraction and Representation Pipeline1hPractical Exercise: Patients and time-series data extraction of MIMIC-III1hCreation of Benchmark data for DNN50mPractical Exercise: Pre-processing of MIMIC-III dataset1hPractical Exercise: One-hot encoding and in-hospital mortality prediction1hPractical Exercise: in-hospital mortality prediction using one-hot encoding and undersampling1hPractical Exercise: Mean vs Joint modelling imputation1hImputation based on moments30m

1 ejercicio de práctica

End of week 3 quiz30m

Semana4

Semana 4

8 horas para completar

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 horas para completar

4 videos (Total 52 minutos), 5 lecturas, 2 cuestionarios

4 videos

Categorical and Continuous Variables19mBayesian Target Encoding6mEncodings Inspired from NLP16mOther Types of Embeddings10m

5 lecturas

Practical Exercise: mean target encoding1hPractical exercise: leave one out encoding1hRepresentation Learning for Electronic Health Records1hPractical Exercise: encoding using an autoencoder1hSimilarity Encodings50m

2 ejercicios de práctica

End of week 4 quiz30mEnd of course summative quiz30m

Acerca de Programa especializado: 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.

The main areas that would explore are: Data mining of Clinical Databases: Ethics, MIMIC III database, International Classification of Disease System and definition of common clinical outcomes. Deep learning in Electronic Health Records: From descriptive analytics to predictive analytics Explainable deep learning models for healthcare applications: What it is and why it is needed Clinical Decision Support Systems: Generalisation, bias, ‘fairness’, clinical usefulness and privacy of artificial intelligence algorithms.

Informed Clinical Decision Making using Deep Learning

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Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
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Deep learning in Electronic Health Records - CDSS 2

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás

Instructor

Fani Deligianni

Ofrecido por

University of Glasgow

Programa - Qué aprenderás en este curso

Artificial Intelligence and Multi-Layer Perceptron

Convolutional and Recurrent Neural Networks.

Preprocessing and imputation of MIMIC III data

EHR Encodings for machine learning models

Acerca de Programa especializado: Informed Clinical Decision Making using Deep Learning

Preguntas Frecuentes

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Deep learning in Electronic Health Records - CDSS 2

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás

Instructor

Fani Deligianni

Ofrecido por

University of Glasgow

Programa - Qué aprenderás en este curso

Artificial Intelligence and Multi-Layer Perceptron

Convolutional and Recurrent Neural Networks.

Preprocessing and imputation of MIMIC III data

EHR Encodings for machine learning models

Acerca de Programa especializado: Informed Clinical Decision Making using Deep Learning

Preguntas Frecuentes

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