Interpretation of Best Medical Coding Practices by Case-Based Reasoning - A User Assistance Prototype for Data Collection for Cancer Registries

International audienceIn the fight against cancer, cancer registries are an important tool. At the heart of these registries is the data collection and coding process. This process is ruled by complex international standards and numerous best practices, which can easily overwhelm (coding) operators. In this paper, a system assisting operators in the interpretation of best medical coding practices and a short evaluation are presented. By leveraging the arguments used by the coding experts to determine the best coding option, the proposed system answers coding questions from operators and provides a partial explanation for the proposed solution

Case-Based Interpretation of Best Medical Coding Practices — Application to Data Collection for Cancer Registries

International audienceCancer registries are important tools in the fight against cancer. At the heart of these registries is the data collection and coding process. Ruled by complex international standards and numerous best practices, operators are easily overwhelmed. In this paper, a system is presented to assist operators in the interpretation of best medical coding practices. By leveraging the arguments used by the coding experts to determine the best coding option, the proposed system is designed to answer the coding questions from operators and provide an answer associated with a partial explanation for the proposed solution

Explanations and Case-Based Reasoning: Foundational Issues

Abstract. By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial knowledge. Fur-ther knowledge can then be added manually or learned over time. CBR systems are not addressing a special group of users. Expert systems, on the other hand, are intended to solve problems similar to human ex-perts. Because of the complexity and difficulty of building and using expert systems, research in this area addressed generating explanations right from the beginning. But for knowledge-intensive CBR applications, the demand for explanations is also growing. This paper is a first pass on examining issues concerning explanations produced by CBR systems from the knowledge containers perspective. It discusses what naturally can be explained by each of the four knowledge containers (vocabulary, similarity measures, adaptation knowledge, and case base) in relation to scientific, conceptual, and cognitive explanations.