Geng Wenye, Qin Xuanfeng, Yang Tao, Cong Zhilei, Wang Zhuo, Kong Qing, Tang Zihui, Jiang Lin
Department of Integrative Medicine, Fudan University Huashan Hospital, Shanghai, China.
Department of Neurosurgery, Fudan University Huashan Hospital, Shanghai, China.
JMIR Med Inform. 2020 Dec 21;8(12):e23082. doi: 10.2196/23082.
Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge.
The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms.
A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms.
The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms.
The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group's sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods.
ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908.
整合医学是一种将替代医学的实践与治疗方法与传统医学相结合的医学形式。整合医学中的诊断包括基于现代医学的临床诊断和证型诊断。电子病历（EMR）是对患者健康信息进行系统化收集，并以数字格式存储，可在不同医疗环境中共享。尽管可以从电子病历中提取症状、体征信息或相关信息，并且可以使用自然语言处理技术将文本内容映射到可计算向量，但应用人工智能技术来支持医生的医疗实践仍然是一项重大挑战。
本研究旨在探讨基于电子病历和自然语言处理的整合医学临床诊断的基于模型的推理（MBR）算法。我们还使用MBR算法估计了样本量、证型类型数量和现代医学诊断之间的关联。
从电子病历中提取了总共14075份临床病例的医疗记录作为开发数据集，并从独立的电子病历中提取了由1000份临床病例医疗记录组成的外部测试数据集。开发了基于词嵌入、机器学习和深度学习算法的MBR方法，用于整合医学中证型的自动诊断。还开发了结合基于规则推理（RBR）的MBR算法。使用由准确率、精确率、召回率和F1分数组成的标准评估指标来评估这些方法的性能。使用最佳算法对样本量、证型类型数量和肺部疾病诊断进行关联分析。
Word2Vec卷积神经网络（CNN）MBR算法在肺部疾病证型诊断中表现出高性能（测试数据集中准确率为0.9586）。结合RBR的Word2Vec CNN MBR也表现出高性能（测试数据集中准确率为0.9229）。肺部疾病的诊断可以提高Word2Vec CNN MBR算法的性能。每组样本量和证型类型都会影响这些算法的性能。
基于Word2Vec和CNN的MBR方法在整合医学肺部疾病证型诊断中表现出高性能。每组样本量、证型类型和肺部疾病诊断的参数与这些方法的性能相关。
ClinicalTrials.gov NCT03274908；https://clinicaltrials.gov/ct2/show/NCT03274908。
