ArticleInfection and drug resistance2025
Establishment of a Machine Learning-Based Predictive Model for
Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To investigate the clinical and ultrasonographic characteristics of pyogenic liver abscess (PLA) caused by Materials and Methods: In this retrospective study, patients clinically diagnosed with PLA and confirmed by ultrasound-guided puncture at the Fifth Medical Center of PLA General Hospital between April 2013 and December 2020 were enrolled. Based on the causative pathogens, patients were categorized into K-PLA and non-K-PLA groups. Baseline data, including ultrasonographic features, clinical characteristics, and laboratory findings, were collected. The Boruta algorithm was employed for feature selection, and four machine learning models-Deep Learning-Fully Connected Neural Network (deeplearning), Distributed Random Forest (drf), Gradient Boosting Machine (gbm), and Generalized Linear Model (glm)-were developed to diagnose K-PLA. The models were validated using 5-fold cross-validation. Results: A total of 201 patients with bacterial liver abscess were included (median age: 57 years; range: 49-66; 136 males), comprising 134 K-PLA cases and 67 non-K-PLA cases. The Boruta algorithm identified seven significant predictive variables: history of diabetes, history of hepatocellular carcinoma, history of biliary tract disease, history of infectious diseases, duration of fever, body temperature, and alanine aminotransferase (ALT) levels. Using these variables, the four machine learning models were constructed. In the training set, the area under the receiver operating characteristic curve (AUC) for predicting K-PLA was 0.716 (deeplearning), 0.999 (drf), 0.922 (gbm), and 0.718 (glm). In the validation set, the corresponding AUC values were 0.799, 0.763, 0.848, and 0.805, respectively. Conclusion: This study successfully established four machine learning models for predicting the risk of K-PLA, with the gbm-based model demonstrating the highest diagnostic performance. These models may facilitate early clinical diagnosis and treatment of K-PLA, thereby reducing antibiotic misuse.
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.