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Informed Clinical Decision Making using Deep Learning

Data Mining of Clinical Databases


  • week01:
    • Big Data in Healthcare
    • EHR System in the UK and USA
    • MIMIC Critical Care Dataset: The Impact
    • Data Usage Requirements
  • week02:
    • MIMIC-III Data Linkage
    • MIMIC-III as a Relational Database
    • MIMIC-III - Descriptive Statistics
    • Vital Signs Extraction for a Single Patient
  • week03
    • Introduction to International Classification of Disease System
    • Evolution of the ICD System
    • ICD-9 and MIMIC-III
    • From ICD-9 to ICD-10 (ICD-11)
  • week04
    • Concepts in MIMIC III
    • Relation of Catheterization to Mortality: An Example Study
    • Data Extraction

Deep Learning in Electronic Health Records


  • week01:
    • Deep Learning and Artificial Intelligence
    • Multi-Layer Perceptron
    • Optimization of a Multi-Layer Perceptron (Part 1)
    • Optimization of a Mutli-Layer Perceptrion (Part 2)
    • Preprocessing of ECG Signal
  • week02:
    • Validation of Machine Learning Models
    • Convolutional Neural Networks
    • Recurrent Neural Networks
  • week03
    • Benchmark Deep Learning Models with EHR - Part 1
    • Benchmark Deep Learning Models with EHR - Part 2
    • Imputation Strategies
    • Deep Learning Imputation Strategies
  • week04
    • Categorical and Continuous Variables
    • Bayesian Target Encoding
    • Encodings Inspired from NLP
    • Other Types of Embeddings

Explainable Deep Learning Models for Healthcare


  • week01:
    • Interpretability vs Explainability
    • 'Explainability' in Healthcare Applications
    • Taxonomy of Explainability Methods
    • Model Agnostic Explainability Methods
    • Permutation Feature Importance in Time Series Data
  • week02:
    • Local Interpretable Model Agnostic Explanations (LIME)
    • LIME in Time-Series Classification
    • Shapley Additive Explanations
    • Model-Specific Explanations: Visualisation Methods
    • CAM in Time-Series Classification
  • week03
    • Gradient Weighted Class Activation Maps
    • Grad-CAM in Time-Series Classification
    • Integrated Gradients
    • Integrated Gradients in Time Series Classification
  • week04
    • Attention in Deep Learning
    • Taxonomy of Attention
    • Attention and Explainability

Clinical Decision Support Systems


  • week01:
    • From Reproducibility to Generalisability
    • A Guide to Model Validation in Clinical Decision Support Systems
    • Calibration of Deep Learning Models
  • week02:
    • Assessment of the Risk of Bias in EHR
    • Fairness in Machine Learning for Healthcare Applications (Part 1)
    • Fairness in Machine Learning for Healthcare Applications (Part 2)
  • week03
    • Decision Curve Analysis
    • Human-Centered Clinical Decision Support Systems
    • Evaluation of Explainability Models
  • week04
    • Privacy Concerns in CDSS
    • Defences Against Inference Attacks
    • Adversarial Attacks - Explainability

Acknowledgements

  • Hester Huijsdens for her meticulous work in adopting, simplifying and improving related python notebooks
  • Yola Jones, Shourya Verma and Hester Huijsdens for their work on explainable machine learning models
  • Samuel Leighton for his insight in Clinical Decision Support Systems

Publications

  • D. Ravi, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, G. Z. Yang, Deep Learning for Health Informatics, IEEE Journal of Biomedical and Health Informatics, 21(1), 2017.
  • J. Andreu-Perez, F. Deligianni, D. Ravi, G. Z. Yang, Artificial Intelligence and Robotics, UK-RAS White Papers, 2017.
  • Y. Jones, F. Deligianni and J Dalton, Improving ECG Classification Interpretability Using Saliency Maps, IEEE BIBE, 2020. (Best paper award)
  • M Podjaski and F. Deligianni, ‘Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition’, IEEE-SSCI 2021, https://arxiv.org/abs/2105.03958.
  • Leighton et al. Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach, The Lancet: Digital Health, 2019.
  • Leighton et al. Development and validation of a non remission risk prediction model in First Episode Psychosis: An analysis of two longitudinal studies, BJPsych Open, 2021.
  • Lee et al. Prediction models in first episode psychosis: a systematic review and critical appraisal, British Journal of Psychiatry, 2022.

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