An interpretable knowledge-based decision support system and its applications in pregnancy diagnosis

This paper aims to propose an interpretable knowledge-based decision support system (IKBDSS) that will assist physicians to predict the risk level of a disease. Our system enables to integrate both historical cases extracted from database and opinions provided by different experts in order to set up a medical knowledge base and provide relevant advises by inferring from the knowledge base. To present various experts' opinions, the Multi-granularity Linguistic Term Sets (MLTS) model is used to address the ambiguity and intangibility of knowledge. Our work mainly focuses on knowledge acquisition, similarity degree calculation and consistency checking process. It is worth mentioning that a criterion weights calculation method is introduced to objectively obtain the weights based on knowledge from experts, rather than subjectively predefined. The developed system leads to a better performance in specificity, sensitivity and F-1 score compared to other methods in the literature. To conclude, our work contributes to: (1) The development of a medical decision support system to combine clinical records and domain knowledge to predict diagnosis. (2) The decision-making process ensures interpretability, which increases the reliability of our system in terms of being a decision supporter. (3) The criterion weights are calculated based on the professional knowledge presented in MLTS form, and this process improves the capacity of providing diagnostic recommendations. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces an interpretable medical decision support system that integrates historical cases and expert opinions to establish a medical knowledge base for providing relevant recommendations. The system outperforms others in terms of specificity, sensitivity, and F-1 score, contributing to reliable support for diagnosis prediction.