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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.

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What's inside

Five courses

Data mining of Clinical Databases - CDSS 1

(0 hours)

This course introduces MIMIC-III, the largest publicly available EHR database. You will learn about its design, query tools, and how to extract and visualize descriptive analytics. Understanding the schema and ICD coding is crucial for mapping research questions to data and extracting key clinical outcomes for developing clinically useful machine learning algorithms.

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Deep learning in Electronic Health Records - CDSS 2

(0 hours)

Overview of Deep Learning principles and architectures. Formulate time-series classification problems for vital signals like ECG. Apply these methods to Electronic Health Records, addressing missing values and heterogeneity. Explore imputation techniques and encoding strategies. Formulate clinical prediction benchmarks from MIMIC-III data.

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Explainable deep learning models for healthcare - CDSS 3

(0 hours)

This course introduces interpretability and explainability in machine learning applications. Learners will understand 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.

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Clinical Decision Support Systems - CDSS 4

(0 hours)

Machine learning systems in Clinical Decision Support Systems (CDSS) require external validation, calibration analysis, and bias and fairness assessment. This course covers machine learning evaluation concepts used in CDSS, decision curve analysis, and human-centered CDSS design. It also addresses privacy concerns and adversarial attacks in deep learning models, and explores the future of explainable and privacy-preserved CDSS.

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Capstone Assignment - CDSS 5

This capstone assignment requires you to apply the knowledge and skills you have learned throughout the specialization. You will choose one of the areas and complete the assignment to pass.

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Learning objectives

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

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

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Time

110 hours

Level

Intermediate

Via

Coursera

Institution

University of Glasgow

Instructor

Fani Deligianni

Level

Intermediate

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