This specialisation is designed for experienced programmers seeking to enhance their skills in applying deep learning techniques to Electronic Health Records (EHR) and building Clinical Decision Support Systems (CDSS) for healthcare applications.
The course delves into data mining of clinical databases, deep learning in EHR, explainable deep learning models for healthcare, and the development of CDSS. Participants will learn to extract, preprocess, and apply deep learning to complex clinical databases, as well as understand the ethical considerations and privacy concerns surrounding AI algorithms in healthcare.
Throughout the program, learners will have the opportunity to work on a capstone assignment, applying the knowledge and skills acquired throughout the specialization.
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This course explores data mining of clinical databases, deep learning in Electronic Health Records, explainable deep learning models for healthcare applications, Clinical Decision Support Systems, and a capstone assignment.
Understand the schema of publicly available EHR databases (MIMIC-III) and the use of International Classification of Diseases (ICD) system.
Extract and visualize descriptive statistics from clinical databases, and recognize key clinical outcomes such as mortality and length of stay.
Train deep learning architectures for classification, validate and compare different machine learning algorithms, and preprocess EHR data as time-series data.
Learn imputation strategies and data encodings for EHR.
Program global and local explainability methods for deep learning, understand axiomatic attributions for deep learning networks, and incorporate attention in Recurrent Neural Networks.
Visualize the attention weights and program explainability methods for time-series classification.
Evaluate Clinical Decision Support Systems, analyze bias and fairness in machine learning models, and understand decision curve analysis.
Explore human-centered CDSS and address privacy concerns in healthcare decision support systems.
Complete a capstone assignment applying the knowledge and skills gained throughout the specialization.
Choose one area and complete the assignment to pass the course.
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