SKET X: A Visual Analytics Tool for Explaining Knowledge Extraction Results

In recent years, knowledge extraction approaches have been adopted to distill the medical knowledge included in clinical reports. In this regard, the Semantic Knowledge Extractor Tool (SKET) has been introduced for extracting knowledge from pathology reports, leveraging a hybrid approach that combines unsupervised rule-based techniques with pre-trained Machine Learning (ML) models. Since ML models are usually based on probabilistic/statistical approaches, their predictions cannot be easily understood, especially for what concerns their underlying decision mechanism. To explain the SKET's decisionmaking process, we propose SKET eXplained (SKET X), a web-based system providing visual explanations in terms of the models, rules, and parameters involved for each prediction. SKET X is designed for pathologists and experts to ease the comprehension of SKET predictions, increase awareness, and improve the effectiveness of the overall knowledge extraction process according to the pathologists' ...