ArticleCommunications medicine2025
Monitoring for early prediction of gram-negative bacteremia using machine learning and hematological data in the emergency department.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to develop an artificial intelligence-assisted tool for the prediction of Gram-negative bacteremia, using cell population data, complete blood count, and differential count. The model seeks to distinguish among nonbacteremia, Gram-negative bacteremia, and Gram-positive bacteremia in patients presenting to the emergency department.
methodsThis retrospective study was conducted in the emergency departments of three hospitals in Taiwan. Data from adults with suspected bacterial infections were collected, including complete blood count, white blood cell differential count, and cell population data. A gradient boosting model (Catboost) was developed to classify nonbacteremia, Gram-negative and Gram-positive bacteremia. We evaluated the model through discrimination and calibration.
resultsHere, we show an analysis of 28,503 cases from the China Medical University Hospital developing cohort, including 795 cases of Gram-positive and 2174 cases of Gram-negative bacteremia. Validation cohorts comprise 15,801 cases from China Medical University Hospital, 2632 from Wei-Gong Memorial Hospital, and 3811 from An-Nan Hospital. For Gram-negative bacteremia, the area under the receiver operating characteristic curve ranges from 0.861 to 0.869, with values for the area under the precision-recall curve ranging from 0.325 to 0.415. Predictions for Gram-positive bacteremia are less accurate, with areas under the curve ranging from 0.759 to 0.798 and values between 0.079 and 0.093 for the precision-recall curve.
conclusionsThis study shows that machine learning using hematological parameters provides robust early detection of Gram-negative bacteremia in emergency department settings. Cell population data are valuable predictors by reflecting host immune responses. Data imbalance and marked blood cell changes in Gram-negative bacteria may hinder recognition of Gram-positive bacteremia. Future research should explore the real-world impact of deploying the model in clinical settings.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.