Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

TL;DR

This paper presents an ontology-based decision support system for cancer treatment that uses case-based reasoning and natural language processing to improve diagnosis accuracy and provide treatment recommendations.

Contribution

It introduces an ontology-enhanced DSS integrating case-based reasoning and NLP for cancer treatment, improving disease classification accuracy and decision support.

Findings

Achieved 84.63% accuracy in disease classification

Utilized Disease Ontology to enhance reasoning capabilities

Supported natural language queries for clinical decision support

Abstract

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.