- Deep Learning
- Machine Learning
- Explainable Machine Learning
- processing electronic health records
- clinical decision support systems
- International Classification of Diseases
- mining clinical databases
- Descriptive Statistics
- Electronic Health Records
- Ethics in EHR
- preprocessing of EHR and imputation
- Convolutional Neural Network
Programa especializado: Informed Clinical Decision Making using Deep Learning
Apply Deep Learning in Electronic Health Records. Understand the road path from data mining of clinical databases to clinical decision support systems
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Programa especializado: Informed Clinical Decision Making using Deep LearningUniversity of Glasgow Qué aprenderás
Extract and preprocess data from complex clinical databases
Apply deep learning in Electronic Health Records
Imputation of Electronic Health Records and data encodings
Explainable, fair and privacy-preserved Clinical Decision Support Systems
Habilidades que obtendrás
Acerca de este Programa Especializado
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.
Proyecto de aprendizaje aplicado
Learners have the opportunity to choose and undertake an exercise based on MIMIC-III extracted datasets that combines knowledge from:
Data mining of Clinical Databases to query the MIMIC database
Deep learning in Electronic Health Records to pre-process EHR and build deep learning models
Explainable deep learning models for healthcare to explain the models decision
Learners can choose from:
1. Permutation feature importance on the MIMIC critical care database
The technique is applied both on logistic regression and on an LSTM model. The explanations derived are global explanations of the model.
2. LIME on the MIMIC critical care database
The technique is applied on both logistic regression and an LSTM model. The explanations derived are local explanations of the model.
3. Grad-CAM on the MIMIC critical care database
GradCam is implemented and applied on an LSTM model that predicts mortality. The explanations derived are local explanations of the model.
Certificado para compartir
Obtén un certificado al finalizar
Cursos 100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Cronograma flexible
Establece y mantén fechas de entrega flexibles.
Nivel intermedio
Last year undergraduate or master students of computing science or engineering. Basic knowledge on SQL queries and python is required.
Aproximadamente 5Â meses para completarse
Ritmo sugerido de 5Â horas/semana
Inglés (English)
SubtÃtulos: Inglés (English)
Certificado para compartir
Obtén un certificado al finalizar
Cursos 100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Cronograma flexible
Establece y mantén fechas de entrega flexibles.
Nivel intermedio
Last year undergraduate or master students of computing science or engineering. Basic knowledge on SQL queries and python is required.
Aproximadamente 5Â meses para completarse
Ritmo sugerido de 5Â horas/semana
Inglés (English)
SubtÃtulos: Inglés (English)
Cómo funciona el programa especializado
Toma cursos
Un programa especializado de Coursera es un conjunto de cursos que te ayudan a dominar una aptitud. Para comenzar, inscrÃbete en el programa especializado directamente o échale un vistazo a sus cursos y elige uno con el que te gustarÃa comenzar. Al suscribirte a un curso que forme parte de un programa especializado, quedarás suscrito de manera automática al programa especializado completo. Puedes completar solo un curso: puedes pausar tu aprendizaje o cancelar tu suscripción en cualquier momento. Visita el panel principal del estudiante para realizar un seguimiento de tus inscripciones a cursos y tu progreso.
Proyecto práctico
Cada programa especializado incluye un proyecto práctico. Necesitarás completar correctamente el proyecto para completar el programa especializado y obtener tu certificado. Si el programa especializado incluye un curso separado para el proyecto práctico, necesitarás completar cada uno de los otros cursos antes de poder comenzarlo.
Obtén un certificado
Cuando completes todos los cursos y el proyecto práctico, obtendrás un Certificado que puedes compartir con posibles empleadores y tu red profesional.
Hay 5 cursos en este Programa Especializado
Data mining of Clinical Databases - CDSS 1
This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics.
Deep learning in Electronic Health Records - CDSS 2
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.
Explainable deep learning models for healthcare - CDSS 3
This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification. Subsequently, model-specific explanations such as Class-Activation Mapping (CAM) and Gradient-Weighted CAM are explained and implemented. The learners will understand axiomatic attributions and why they are important. Finally, attention mechanisms are going to be incorporated after Recurrent Layers and the attention weights will be visualised to produce local explanations of the model.
Clinical Decision Support Systems - CDSS 4
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.
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.
Preguntas Frecuentes
¿Cuál es la polÃtica de reembolsos?
¿Puedo inscribirme en un solo curso?
¿Hay ayuda económica disponible?
¿Puedo tomar este curso de manera gratuita?
¿Este curso es 100 % en lÃnea? ¿Necesito asistir a alguna clase en persona?
¿Cuánto tiempo se necesita para completar un programa especializado?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
¿Recibiré crédito universitario por completar el programa especializado?
What will I be able to do upon completing the Specialization?
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