PDF(Pubmed)
Abstract：
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: The experimental results indicate that the method achieved 99.4% accuracy, 98.5% sensitivity, 99.6% specificity, 98.7% precision, 98.6% F1-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.
CONCLUSIONS: This method provides doctors with a reliable and explainable diagnostic tool that is expected to improve tinnitus diagnosis accuracy.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: The experimental results indicate that the method achieved 99.4% accuracy, 98.5% sensitivity, 99.6% specificity, 98.7% precision, 98.6% F1-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.
CONCLUSIONS: This method provides doctors with a reliable and explainable diagnostic tool that is expected to improve tinnitus diagnosis accuracy.
摘要：
背景：耳鸣诊断由于极其复杂的发病机制而在耳鼻咽喉科中提出了挑战，缺乏有效的客观化方法，和因素影响的诊断。目前在临床实践中缺乏可解释的耳鸣辅助诊断工具。
目的：本研究旨在使用可解释的人工智能（AI）方法开发诊断模型，以解决耳鸣诊断中准确性低的问题。
方法：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的耳鸣诊断方法。将1267例患者的电子病历数据与传统中医临床医学知识相结合,构建耳鸣知识图谱。随后，重量被引入，基于互信息值测量知识图中的患者相似度。最后,提出了一种协作邻居算法，对患者相似性进行评分以获得推荐诊断。我们进行了2组实验和1个案例推导，以探索我们模型的有效性，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了比较。
结果：实验结果表明，该方法达到了99.4％的准确性，98.5%灵敏度,99.6%的特异性，精度98.7%,98.6%F1得分,在253名测试患者中，推断5种耳鸣亚型的受试者工作特征曲线下的面积为99％。此外,它表现出良好的可解释性。知识图的拓扑结构提供了透明度，可以解释患者之间相似的原因。
结论：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。
目的：本研究旨在使用可解释的人工智能（AI）方法开发诊断模型，以解决耳鸣诊断中准确性低的问题。
方法：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的耳鸣诊断方法。将1267例患者的电子病历数据与传统中医临床医学知识相结合,构建耳鸣知识图谱。随后，重量被引入，基于互信息值测量知识图中的患者相似度。最后,提出了一种协作邻居算法，对患者相似性进行评分以获得推荐诊断。我们进行了2组实验和1个案例推导，以探索我们模型的有效性，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了比较。
结果：实验结果表明，该方法达到了99.4％的准确性，98.5%灵敏度,99.6%的特异性，精度98.7%,98.6%F1得分,在253名测试患者中，推断5种耳鸣亚型的受试者工作特征曲线下的面积为99％。此外,它表现出良好的可解释性。知识图的拓扑结构提供了透明度，可以解释患者之间相似的原因。
结论：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。
