ArticleNPJ digital medicine2026
Development and validation of a machine learning-based diagnostic system for 22 pediatric respiratory pathogens: a large-scale multicenter study.
Article in NPJ digital medicine, 2026. 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.
- Pathogen prevalence, feature composition and cross-centre generalisability of machine learning diagnostic models for multi-pathogen respiratory infection.Frontiers in public health · 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
19 authors.
Funding
Abstract
Respiratory tract infections (RTIs) are a significant cause of morbidity in children, caused by a wide range of pathogens. As treatment strategies depend on the causative pathogen, early and accurate diagnosis is crucial. We developed and validated an interpretable Pathogen Diagnostic System for Pediatric Respiratory Infections (Pathog-PDx), conducting a multicenter study involving 134,500 hospitalized children across three clinical centers and two databases. The model integrated 42 clinical and laboratory features from electronic health records (EHR) to enable early pathogen identification. Prospective validation was carried out on an independent cohort of 1338 children to assess the real-world applicability of the model. Pathog-PDx accurately distinguished 22 pathogen subtypes and outperformed conventional models in identifying both single and mixed infections. The model achieved high classification performance for key pathogens, such as influenza virus (AUC = 0.95; Sn: 0.88; Sp: 0.86), with mean AUCs of 0.88 for various pathogens of RTIs. The model has been deployed as a web-based decision support system, which is freely accessible at https://pathogpdx.zzu.edu.cn . Altogether, Pathog-PDx represents a potential tool for the early identification of respiratory tract pathogens in pediatric patients, which can provide actionable predictions ahead of conventional test results to guide timely and targeted therapy.
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.