Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
This work addresses the need for formal and consistent treatment recommendations for physicians in cancer care, though it is incremental as it combines existing methods like ontologies and CBR.
The paper tackled the problem of inconsistent interpretation in natural language clinical decision support by constructing an ontology-based system using Case-Based Reasoning, achieving 84.63% accuracy in disease classification.
Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology.