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.