Exploring FUO Intelligent Diagnosis Based on Clinical Data.

BACKGROUND Fever of Unknown Origin (FUO) is a group of diseases with heterogeneous complex causes that are misdiagnosed or have delayed diagnoses. Previous studies have mainly focused 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. OBJECTIVE We aimed to fuse all 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. METHODS 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 health record (EHR) data using the Encoder Representations from Transformers (BERT) model. Next, we extracted the features based on the structured sample data and trained the FID using the LightGBM. RESULTS Experiments were based on data from 2299 desensitized cases from Peking Union Medical College Hospital. From the extensive experiments, the precision of the FID was 81.68% for top 1 classification diagnosis and 96.17% for top 2 classification diagnoses, which were superior to the precisions of the comparative method. In addition, we found that for FUO tumor patients, their average age was higher than those of other categories, and there were more female patients with FUO immune diseases. CONCLUSIONS In conclusion, the FID showed excellent performance in FUO diagnosis, and it would be meaningful for clinicians to enhance the precision of FUO diagnosis and reduce the misdiagnosis rate.