Overview

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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.

Syllabus

Course 1: Data mining of Clinical Databases - CDSS 1
- Offered by University of Glasgow . This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) ... Enroll for free.

Course 2: Deep learning in Electronic Health Records - CDSS 2
- Offered by University of Glasgow . Overview of the main principles of Deep Learning along with common architectures. Formulate the problem ... Enroll for free.

Course 3: Explainable deep learning models for healthcare - CDSS 3
- Offered by University of Glasgow . This course will introduce the concepts of interpretability and explainability in machine learning ... Enroll for free.

Course 4: Clinical Decision Support Systems - CDSS 4
- Offered by University of Glasgow . Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external ... Enroll for free.

Course 5: Capstone Assignment - CDSS 5
- Offered by University of Glasgow . This course is a capstone assignment requiring you to apply the knowledge and skill you have learnt ... Enroll for free.

Courses

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20 hours 54 minutes
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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. The schema and International Classification of Diseases coding is important to understand how to map research questions to data and how to extract key clinical outcomes in order to develop clinically useful machine learning algorithms.
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1 day 7 hours 38 minutes
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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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2 hours 30 minutes
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This course is a capstone assignment requiring you to apply the knowledge and skill you have learnt throughout the specialization. In this course you will choose one of the areas and complete the assignment to pass.
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1 day 6 hours 5 minutes
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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.
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8 hours 10 minutes
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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.