Explainable artificial intelligence typically focuses on data-based explanations, lacking the
semantic context needed to produce human-centric explanations. This is especially relevant …

Due to their tremendous potential in predictive tasks, Machine Learning techniques such as
Artificial Neural Networks have received great attention from both research and practice …

This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …

Explaining increasingly complex machine learning will remain crucial to cope with risks,
regulations, responsibilities, and human support in healthcare. However, extant explainable …

Background Capturing complete medical knowledge is challenging-often due to incomplete
patient Electronic Health Records (EHR), but also because of valuable, tacit medical …

The complexity of neurodegenerative diseases has motivated the development of artificial
intelligence approaches to predicting risk of impairment and disease progression. However …

Artificial Intelligence based algorithms are gaining a main role in healthcare. However, the
black-box nature of models such as deep neural networks challenges the users' trust …

Plausible reasoning is an interesting and viable approach for semantic data analytics as it
provides a non-deterministic and exploratory approach to inferring new knowledge from …

Explainability has been an important goal since the early days of Artificial Intelligence.
Several approaches for producing explanations have been developed. However, many of …

Background Clinical phenotypes and disease-risk stratification are most often determined
through the direct observations of clinicians in conjunction with published standards and …