The information in pathology diagnostic reports is often encoded in natural language. Extracting such knowledge can be instrumental in developing clinical decision support systems. However, the digital pathology domain lacks knowledge extraction systems suited to the task. One of the few examples is the Semantic Knowledge Extractor Tool (SKET), a hybrid knowledge extraction system combining a rule-based expert system with pre-trained ML models. SKET has been designed to extract knowledge from colon, cervix, and lung cancer diagnostic reports. To do so, the system employs an ontology-driven approach, where the extracted entities are linked with concepts modeled through a reference ontology, namely, the ExaMode ontology. In this work, we adapt SKET to a newer version of the ExaMode ontology and extend the method to account for an additional use case: Celiac disease. Our experimental results show that: 1) the new version of SKET outperforms the previous one on colon, cervix, and lung canc...

An Ontology-Driven Knowledge Extraction Tool for Pathology Record Classification

Menotti L.;Marchesin S.;Silvello G.
2023

Abstract

The information in pathology diagnostic reports is often encoded in natural language. Extracting such knowledge can be instrumental in developing clinical decision support systems. However, the digital pathology domain lacks knowledge extraction systems suited to the task. One of the few examples is the Semantic Knowledge Extractor Tool (SKET), a hybrid knowledge extraction system combining a rule-based expert system with pre-trained ML models. SKET has been designed to extract knowledge from colon, cervix, and lung cancer diagnostic reports. To do so, the system employs an ontology-driven approach, where the extracted entities are linked with concepts modeled through a reference ontology, namely, the ExaMode ontology. In this work, we adapt SKET to a newer version of the ExaMode ontology and extend the method to account for an additional use case: Celiac disease. Our experimental results show that: 1) the new version of SKET outperforms the previous one on colon, cervix, and lung canc...
2023
Proceedings of the 31st Symposium on Advanced Database System, July 02–05, 2023, Galzignano Terme, Padua, Italy
31st Symposium of Advanced Database Systems, SEBD 2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3508108
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