Jiang Huizhen, Li Yuanjie, Zeng Xuejun, Xu Na, Zhao Congpu, Zhang Jing, Zhu Weiguo
Department of Information Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Primary Care and Family Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
JMIR Med Inform. 2020 Nov 30;8(11):e24375. doi: 10.2196/24375.
Fever of unknown origin (FUO) is a group of diseases with heterogeneous complex causes that are misdiagnosed or have delayed diagnoses. Previous studies have focused mainly on the statistical analysis and research of the cases. The treatments are very different for the different categories of FUO. Therefore, how to intelligently diagnose FUO into one category is worth studying.
We aimed to fuse all of the medical data together to automatically predict the categories of the causes of FUO among patients using a machine learning method, which could help doctors diagnose FUO more accurately.
In this paper, we innovatively and manually built the FUO intelligent diagnosis (FID) model to help clinicians predict the category of the cause and improve the manual diagnostic precision. First, we classified FUO cases into four categories (infections, immune diseases, tumors, and others) according to the large numbers of different causes and treatment methods. Then, we cleaned the basic information data and clinical laboratory results and structured the electronic medical record (EMR) data using the bidirectional encoder representations from transformers (BERT) model. Next, we extracted the features based on the structured sample data and trained the FID model using LightGBM.
Experiments were based on data from 2299 desensitized cases from Peking Union Medical College Hospital. From the extensive experiments, the precision of the FID model was 81.68% for top 1 classification diagnosis and 96.17% for top 2 classification diagnosis, which were superior to the precision of the comparative method.
The FID model showed excellent performance in FUO diagnosis and thus would be a potentially useful tool for clinicians to enhance the precision of FUO diagnosis and reduce the rate of misdiagnosis.
不明原因发热（FUO）是一组病因复杂多样的疾病，常被误诊或诊断延迟。以往的研究主要集中在病例的统计分析和研究上。不同类型的FUO治疗方法差异很大。因此，如何智能地将FUO诊断为某一类别值得研究。
我们旨在将所有医学数据融合在一起，使用机器学习方法自动预测FUO患者病因的类别，这有助于医生更准确地诊断FUO。
在本文中，我们创新性地手动构建了FUO智能诊断（FID）模型，以帮助临床医生预测病因类别并提高人工诊断的准确性。首先，根据大量不同的病因和治疗方法，将FUO病例分为四类（感染、免疫疾病、肿瘤和其他）。然后，我们清理了基本信息数据和临床实验室结果，并使用来自变换器的双向编码器表示（BERT）模型对电子病历（EMR）数据进行结构化。接下来，我们基于结构化样本数据提取特征，并使用LightGBM训练FID模型。
实验基于北京协和医院2299例脱敏病例的数据。从广泛的实验来看，FID模型在 top1分类诊断中的准确率为81.68%，在top2分类诊断中的准确率为96.17%，均优于比较方法的准确率。
FID模型在FUO诊断中表现出优异的性能，因此将成为临床医生提高FUO诊断准确性和降低误诊率的潜在有用工具。
