%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 12
%N 
%P e59858
%T Early Diagnosis of Hereditary Angioedema in Japan Based on a US Medical Dataset: Algorithm Development and Validation
%A Yamashita,Kouhei
%A Nomoto,Yuji
%A Hirose,Tomoya
%A Yutani,Akira
%A Okada,Akira
%A Watanabe,Nayu
%A Suzuki,Ken
%A Senzaki,Munenori
%A Kuroda,Tomohiro
%+ Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan, 81 75 751 4964, kouhei@kuhp.kyoto-u.ac.jp
%K machine learning
%K screening
%K AI
%K prediction
%K rare diseases
%K HAE
%K electronic medical record
%K real world data
%K big data
%K angioedema
%K edema
%K ML
%K artificial intelligence
%K algorithm
%K algorithms
%K predictive model
%K predictive models
%K predictive analytics
%K predictive system
%K practical model
%K practical models
%K early warning
%K early detection
%K real world data
%K RWD
%K Electronic health record
%K EHR
%K electronic health records
%K EHRs
%K EMR
%K electronic medical records
%K EMRs
%K patient record
%K patient record
%K health record
%K health records
%K personal health record
%K PHR
%D 2024
%7 13.9.2024
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: Hereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified. Objective: This study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed to validate the detection performance of the model for HAE cases using the Japanese dataset. Methods: The HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability to the Japanese dataset. Results: Precision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, the precision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 of these patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms. Conclusions: This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE. 
%R 10.2196/59858
%U https://medinform.jmir.org/2024/1/e59858
%U https://doi.org/10.2196/59858