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Clinical Decision Support Systems - CDSS 4
  • Level Professional
  • المدة 8 ساعات hours
  • الطبع بواسطة University of Glasgow
  • Offered by

عن

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.

الوحدات

Welcome to 'From machine learning models to clinical decision support systems'

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  • Welcome: From machine learning models to clinical decision support systems

A Guide to Model Validation in CDSS

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  • From Reproducibility to Generalisability

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  • An ABCD guide for prediction model validation in clinical settings

Calibration in Clinical Prediction Models

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  • A Guide to Model Validation in Clinical Decision Support Systems

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  • Calibration: the Achilles heel of predictive analytics

Calibration in Deep Learning Models

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  • Calibration of Deep Learning Models

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  • Bias assessment in Deep Learning Models

End of Week 1

    • 1 Discussion
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  • End of week 1 Quiz

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  • Week 1 - Your experience

Assessment of Risk of Bias in Clinical Studies

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  • Assessment of the Risk of Bias in EHR

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  • PROBAST: A Tool to Assess the Risk of Bias

'Unfairness' in state-of-the-art machine learning models

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  • Fairness in Machine Learning for Healthcare Applications (Part 1)

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  • Big Data's Disparate Impact

Strategies to ensure 'fairness' in machine learning models

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  • Fairness in Machine Learning for Healthcare Applications (Part 2)

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  • Ensuring Fairness in Machine Learning to Advance Health Equity

End of Week 2

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  • End of week 2 Quiz

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  • Week 2 - Your experience

Decision Curve Analysis

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  • Decision Curve Analysis

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  • A Guide to Interpreting Decision Curve Analysis

Ethics in Artificial Intelligence and Clinical Decision Support Systems

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  • Human-Centered Clinical Decision Support Systems

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  • A Roadmap Toward Transparent Expert Companions

Explainability as a central component of Human-Centered CDSS

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  • Evaluation of Explainability Models

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  • The role of explainability in creating trustworthy artificial intelligence for health care

End of week 3

    • 1 Discussion
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  • End of week 3 Quiz

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  • Week 3 - Your experience

Leakage and privacy concerns of deep learning models

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  • Privacy Concerns in CDSS

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  • Leakage and Privacy at Inference Time

Federated Learning and defences against privacy attacks

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  • Defences Against Inference Attacks

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  • Secure, privacy-preserving and federated machine learning

Adversarial attacks against explanations

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  • Adversarial Attacks - Explainability

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  • Explanations can be manipulated

End of week 4

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  • End of week 4 Quiz

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  • Week 4 - Your experience

Clinical Decision Support Systems

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  • End of course summative quiz

Auto Summary

This professional-level course, offered by Coursera, delves into the evaluation of machine learning systems in Clinical Decision Support Systems (CDSS). It covers external validation, calibration analysis, bias assessment, and fairness. Learners will explore decision curve analysis, the need for explainable, human-centered CDSS, and address privacy concerns and adversarial attacks in deep learning models. The course spans 480 minutes and is available through Starter and Professional subscription plans, targeting professionals in Data Science and AI.

Fani Deligianni
Instructor

Fani Deligianni