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A comparison and analysis of explainable clinical decision making using white box and black box models
(2024)
Explainability is a crucial element of machine learning-based making in high stake
scenarios such as risk assessment in criminal justice [80], climate modeling [79], disaster
response [82], education [81] and critical ...
Supporting the executability of R markdown files
(2024)
R Markdown files are examples of literate programming documents that combine R code
with results and explanations. Such dynamic documents are designed to execute easily and
reproduce study results. However, little is ...
Argument summarization: enhancing summary generation and evaluation metrics
(2024)
In the current era of mass digital information, the need for effective argument summarization has become paramount. This thesis explores the domain of argument
summarization, focusing on the development of techniques and ...
Towards accessible healthcare: machine learning-enabled diagnosis of Alzheimer’s disease
(2024)
Alzheimer’s disease poses a critical challenge to public health with an increasing prevalence among the aging population worldwide. The research question is whether machine
learning-based solutions could be a reliable, ...