SKET: an Unsupervised Knowledge Extraction Tool to Empower Digital Pathology Applications

Large volumes of medical data have been produced for decades. These data include diagnoses, which are often reported as free text, thus encoding medical knowledge that is still largely unexploited. To decode the medical knowledge present within reports, we propose the Semantic Knowledge Extractor Tool (SKET), an unsupervised knowledge extraction system combining a rule-based expert system with pretrained Machine Learning (ML) models. This work demonstrates the viability of unsupervised Natural Language Processing (NLP) techniques to extract critical information from cancer reports, opening opportunities such as data mining for knowledge extraction purposes, precision medicine applications, structured report creation, and multimodal learning.