Sharaf-El-Deen Dina A, Moawad Ibrahim F, Khalifa M E
Faculty of Computer and Information Sciences, Ain Shams University, Abbasia, Cairo, Egypt,
J Med Syst. 2014 Feb;38(2):9. doi: 10.1007/s10916-014-0009-1. Epub 2014 Jan 28.
Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.
基于案例的推理（CBR）已应用于许多不同的医学应用中。由于该领域的复杂性和多样性，大多数医学CBR系统都成为了混合系统。此外，CBR中的案例适配过程通常是一个具有挑战性的问题，因为传统上它是由领域专家手动执行的。本文提出了一种用于医学诊断系统的新的混合基于案例的推理方法，以提高仅检索CBR系统的准确性。该方法将基于案例的推理和基于规则的推理相结合，并通过利用适配规则自动应用适配过程。适配规则和推理规则均从案例库中生成。解决新案例后，案例库会扩展，并且适配规则和推理规则都会更新。为了评估所提出的方法，实现了一个原型并进行了实验，以诊断乳腺癌和甲状腺疾病。最终结果表明，所提出的方法提高了仅检索CBR系统的诊断准确性，并且与当前的乳腺癌和甲状腺诊断系统相比提供了可靠的准确性。
