Abstract
In 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.
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Notes
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An adenopathy is an enlargement of lymph nodes, likely due to cancer.
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The morphology describes the type and behavior of the cells that compose the tumor.
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The topography is the location where the tumor originated.
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Acknowledgments
The authors wish to thank the anonymous reviewers of the Joint Workshop on Artificial Intelligence in Health for their remarks which have helped in improving the quality of the paper. The first author would also like to thank the Fondation Cancer for their financial support.
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Schnell, M., Couffignal, S., Lieber, J., Saleh, S., Jay, N. (2019). Interpretation of Best Medical Coding Practices by Case-Based Reasoning—A User Assistance Prototype for Data Collection for Cancer Registries. In: Koch, F., et al. Artificial Intelligence in Health. AIH 2018. Lecture Notes in Computer Science(), vol 11326. Springer, Cham. https://doi.org/10.1007/978-3-030-12738-1_14
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DOI: https://doi.org/10.1007/978-3-030-12738-1_14
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