ArticleFrontiers in cellular and infection microbiology2025
An interpretable machine learning model for early prediction of Escherichia coli infection in ICU patients.
Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Ten key considerations for developing interpretable risk prediction models.Frontiers in bioinformatics · 2026Article
- Development and external validation of a risk prediction model for hypertension among individuals with established hypothyroidism.Frontiers in endocrinology · 2026Article
- Pathogen prevalence, feature composition and cross-centre generalisability of machine learning diagnostic models for multi-pathogen respiratory infection.Frontiers in public health · 2026Article
- Development and validation of an interpretable machine learning-based model for predicting carbapenem-resistantFrontiers in cellular and infection microbiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early and accurate identification of Methods: This retrospective study was conducted using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Adult patients (aged 18-100 years) with their first ICU admission and a length of stay ≥24 hours were included. Results: A total of 52, 554 ICU patients were analyzed, of whom 4, 157 (7.9%) had Conclusions: An interpretable SVM-based machine learning model was developed and validated for early prediction of
Indexed as
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.