Yin Ziming, Kuang Zhongling, Zhang Haopeng, Guo Yu, Li Ting, Wu Zhengkun, Wang Lihua
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Department of Otolaryngology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
JMIR Med Inform. 2024 Jun 10;12:e57678. doi: 10.2196/57678.
Tinnitus diagnosis poses a challenge in otolaryngology owing to an extremely complex pathogenesis, lack of effective objectification methods, and factor-affected diagnosis. There is currently a lack of explainable auxiliary diagnostic tools for tinnitus in clinical practice.
This study aims to develop a diagnostic model using an explainable artificial intelligence (AI) method to address the issue of low accuracy in tinnitus diagnosis.
In this study, a knowledge graph-based tinnitus diagnostic method was developed by combining clinical medical knowledge with electronic medical records. Electronic medical record data from 1267 patients were integrated with traditional Chinese clinical medical knowledge to construct a tinnitus knowledge graph. Subsequently, weights were introduced, which measured patient similarity in the knowledge graph based on mutual information values. Finally, a collaborative neighbor algorithm was proposed, which scored patient similarity to obtain the recommended diagnosis. We conducted 2 group experiments and 1 case derivation to explore the effectiveness of our models and compared the models with state-of-the-art graph algorithms and other explainable machine learning models.
The experimental results indicate that the method achieved 99.4% accuracy, 98.5% sensitivity, 99.6% specificity, 98.7% precision, 98.6% F-score, and 99% area under the receiver operating characteristic curve for the inference of 5 tinnitus subtypes among 253 test patients. Additionally, it demonstrated good interpretability. The topological structure of knowledge graphs provides transparency that can explain the reasons for the similarity between patients.
This method provides doctors with a reliable and explainable diagnostic tool that is expected to improve tinnitus diagnosis accuracy.
耳鸣的诊断在耳鼻喉科是一项挑战，因为其发病机制极其复杂，缺乏有效的客观化方法，且诊断受多种因素影响。目前临床实践中缺乏可解释的耳鸣辅助诊断工具。
本研究旨在使用可解释的人工智能（AI）方法开发一种诊断模型，以解决耳鸣诊断准确性低的问题。
在本研究中，通过将临床医学知识与电子病历相结合，开发了一种基于知识图谱的耳鸣诊断方法。将1267例患者的电子病历数据与中医临床医疗知识整合，构建耳鸣知识图谱。随后引入权重，基于互信息值衡量知识图谱中患者的相似度。最后提出一种协作邻居算法，对患者相似度进行评分以获得推荐诊断。我们进行了2组实验和1例病例推导，以探索模型的有效性，并将模型与最先进的图算法和其他可解释的机器学习模型进行比较。
实验结果表明，该方法在对253例测试患者的5种耳鸣亚型进行推断时，准确率达到99.4%，灵敏度为98.5%，特异性为99.6%，精确率为98.7%，F值为98.6%，受试者工作特征曲线下面积为99%。此外，它还具有良好的可解释性。知识图谱的拓扑结构提供了透明度，能够解释患者之间相似性的原因。
该方法为医生提供了一种可靠且可解释的诊断工具，有望提高耳鸣诊断的准确性。
