Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients

Wei Chun Tsai, Chung Feng Liu, Yu Shan Ma, Chia Jung Chen, Hung Jung Lin, Chien Chin Hsu, Julie Chi Chow, Yu Wen Chien, Chien Cheng Huang

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)

摘要

Background: Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we conducted a study to provide clarity on this issue. Methods: Adult febrile ED patients with blood cultures at Chi Mei Medical Center were divided into derivation (January 2017 to June 2019) and validation groups (July 2019 to December 2020). The derivation group was utilized to develop AI models using twenty-one feature variables and five algorithms to predict bacteremia. The performance of these models was compared with qSOFA score. The AI model with the highest area under the receiver operating characteristics curve (AUC) was chosen to implement the AI prediction system and tested on the validation group. Results: The study included 5,647 febrile patients. In the derivation group, there were 3,369 patients with a mean age of 61.4 years, and 50.7% were female, including 508 (13.8%) with bacteremia. The model with the best AUC was built using the random forest algorithm (0.761), followed by logistic regression (0.755). All five models demonstrated better AUC than the qSOFA score (0.560). The random forest model was adopted to build a real-time AI prediction system integrated into the hospital information system, and the AUC achieved 0.709 in the validation group. Conclusion: The AI model shows promise to predict bacteremia in adult febrile ED patients; however, further external validation in different hospitals and populations is necessary to verify its effectiveness.

原文English
文章編號105176
期刊International Journal of Medical Informatics
178
DOIs
出版狀態Published - 2023 10月

All Science Journal Classification (ASJC) codes

  • 健康資訊學

指紋

深入研究「Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients」主題。共同形成了獨特的指紋。

引用此