ArticleJournal of medical Internet research2020
Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 24 citations in OpenAlex.
- Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients.Scientific reports · 2026Article
- In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records.PloS one · 2024Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation.Scientific reports · 2023Article
- Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art.Journal of intensive care medicine · 2023Article
- Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review.Journal of healthcare informatics research · 2023Review
- An Artificial Intelligence Approach to Bloodstream Infections Prediction.Journal of clinical medicine · 2021Article
- An Attention Model With Transfer Embeddings to Classify Pneumonia-Related Bilingual Imaging Reports: Algorithm Development and Validation.JMIR medical informatics · 2021Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDetecting 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.
objectiveThis 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).
methodsIn 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.
resultsOut 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.
conclusionsA 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.
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