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

Related Books

• M. Alkan, H. Huijsdens, Y. Jones, F. Deligianni, Electronic Health Records: Towards Digital Twins in Healthcare, in 'Smart and Connected Healthcare: Applications and Use Cases', Volume 2, Eds. F. Firouzi, K. Chakrabarty, B. Farahani, Springer, 2025. [https://arxiv.org/abs/2501.09640]
• M. Alkan, I. Zakariyya, S. Leighton, K.B. Sivangi, C. Anagnostopoulos, F. Deligianni, Artificial Intelligence Driven Clinical Decision Support Systems], in 'Smart and Connected Healthcare: Applications and Use Cases', Volume 2, Eds. F. Firouzi, K. Chakrabarty, B. Farahani, Springer, 2025. [https://arxiv.org/abs/2501.09628]

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.