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http://dx.doi.org/10.5392/JKCA.2021.21.12.013

A Study on XAI-based Clinical Decision Support System  

Ahn, Yoon-Ae (한국교통대학교 컴퓨터공학전공)
Cho, Han-Jin (극동대학교 에너지IT공학과)
Publication Information
The Journal of the Korea Contents Association / v.21, no.12, 2021 , pp. 13-22 More about this Journal
Abstract
The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.
Keywords
CDSS; Explainable AI; Machine Learning; Feature-based Model; Diagnostic Prediction;
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