TY -非盟的公园,Hyung骏盟——荣格,Dae在那边AU - Ji, Wonjun AU - Choi, Chang-Min PY - 2020 DA - 2020/8/4 TI - Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study JO - J Med Internet Res SP - e19512 VL - 22 IS - 8 KW - deep learning KW - bacteremia KW - early detection KW - time series KW - recurrent neural network KW - neural network KW - informatics KW - surgery KW - sepsis KW - modeling AB - Background: Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not available for use in general wards, and thus, many clinicians usually rely on practical senses to diagnose postoperative infection. Objective: This study aims to evaluate the performance of continuous monitoring with a deep learning model for early detection of bacteremia for surgical in-patients in the general ward and the intensive care unit (ICU). Methods: In this retrospective cohort study, we included 36,023 consecutive patients who underwent general surgery between October and December 2017 at a tertiary referral hospital in South Korea. The primary outcome was the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) for detecting bacteremia by the deep learning model, and the secondary outcome was the feature explainability of the model by occlusion analysis. Results: Out of the 36,023 patients in the data set, 720 cases of bacteremia were included. Our deep learning–based model showed an AUROC of 0.97 (95% CI 0.974-0.981) and an AUPRC of 0.17 (95% CI 0.147-0.203) for detecting bacteremia in surgical in-patients. For predicting bacteremia within the previous 24-hour period, the AUROC and AUPRC values were 0.93 and 0.15, respectively. Occlusion analysis showed that vital signs and laboratory measurements (eg, kidney function test and white blood cell group) were the most important variables for detecting bacteremia. Conclusions: A deep learning model based on time series electronic health records data had a high detective ability for bacteremia for surgical in-patients in the general ward and the ICU. The model may be able to assist clinicians in evaluating infection among in-patients, ordering blood cultures, and prescribing antibiotics with real-time monitoring. SN - 1438-8871 UR - //www.mybigtv.com/2020/8/e19512 UR - https://doi.org/10.2196/19512 UR - http://www.ncbi.nlm.nih.gov/pubmed/32669261 DO - 10.2196/19512 ID - info:doi/10.2196/19512 ER -