University of Glasgow
Informed Clinical Decision Making using Deep Learning Specialization
University of Glasgow

Informed Clinical Decision Making using Deep Learning Specialization

Apply Deep Learning in Electronic Health Records. Understand the road path from data mining of clinical databases to clinical decision support systems

Taught in English

Some content may not be translated

Fani Deligianni

Instructor: Fani Deligianni

1,920 already enrolled

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Specialization - 5 course series

Get in-depth knowledge of a subject

4.7

(18 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Specialization - 5 course series

Get in-depth knowledge of a subject

4.7

(18 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

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Specialization - 5 course series

Data mining of Clinical Databases - CDSS 1

Course 120 hours4.8 (12 ratings)

What you'll learn

  • Understand the Schema of publicly available EHR databases (MIMIC-III)

  • Recognise the International Classification of Diseases (ICD) use

  • Extract and visualise descriptive statistics from clinical databases

  • Understand and extract key clinical outcomes such as mortality and stay of length

Skills you'll gain

Category: mining clinical databases
Category: Electronic Health Records
Category: Descriptive Statistics
Category: Ethics in EHR
Category: International Classification of Diseases

What you'll learn

  • Train deep learning architectures such as Multi-layer perceptron, Convolutional Neural Networks and Recurrent Neural Networks for classification

  • Validate and compare different machine learning algorithms

  • Preprocess Electronic Health Records and represent them as time-series data

  • Imputation strategies and data encodings

Skills you'll gain

Category: global and local explanations
Category: explainable machine learning models
Category: attention mechanisms
Category: interpretability vs explainability
Category: model-agnostic and model specific models

What you'll learn

  • Program global explainability methods in time-series classification

  • Program local explainability methods for deep learning such as CAM and GRAD-CAM

  • Understand axiomatic attributions for deep learning networks

  • Incorporate attention in Recurrent Neural Networks and visualise the attention weights

Skills you'll gain

Category: Recurrent Neural Network
Category: Convolutional Neural Network
Category: data encodings and autoencoders
Category: preprocessing of EHR and imputation
Category: deep learning and validation

What you'll learn

  • Evaluating Clinical Decision Support Systems

  • Bias, Calibration and Fairness in Machine Learning Models

  • Decision Curve Analysis and Human-Centred Clinical Decision Support Systems

  • Privacy concerns in Clinical Decision Support Systems

Skills you'll gain

Category: Privacy concerns in clinical decision support systems
Category: Bias and fairness in machine learning models
Category: Calibration in machine learning models
Category: clinical decision support systems
Category: Human-centred clinical decision support systems

What you'll learn

Instructor

Fani Deligianni
University of Glasgow
5 Courses4,227 learners

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