ArticlePLoS neglected tropical diseases2022
Constructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Disease.
Article in PLoS neglected tropical diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 12 citations in OpenAlex.
- Immunogenicity and Vaccine Shedding After 1 or 2 Doses of rVSVΔG-ZEBOV-GP Ebola Vaccine (ERVEBO®): Results From a Phase 2, Randomized, Placebo-controlled Trial in Children and Adults.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Trial
- Validation of a Bayesian Ebola Diagnostic Model Using Data from the Tenth Epidemic of 2018 to 2020 in the Democratic Republic of Congo.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.EClinicalMedicine · 2026Review
- Identifying Paediatric Populations with Increased Risk for Oropharyngeal Dysphagia in Acute and Critical Care Settings: A Scoping Review.Dysphagia · 2025Article
- Derivation and Internal Validation of a Mortality Prognostication Machine Learning Model in Ebola Virus Disease Based on Iterative Point-of-Care Biomarkers.Open forum infectious diseases · 2024Article
- Factors associated with death in patients admitted with Ebola virus disease to Ebola Treatment Units in Guinea, Sierra Leone, and Liberia - December 2013 to March 2016.F1000Research · 2024Article
- The Ebola Data Platform: A prospective, standardised, clinical dataset collected during the 2013-2016 West African Ebola outbreak.Wellcome open research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundEbola Virus Disease (EVD) causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on predicting mortality in pediatric patients. Here we present machine learning-derived prognostic models to predict clinical outcomes in children infected with Ebola virus.
methodsUsing retrospective data from the Ebola Data Platform, we investigated children with EVD from the West African EVD outbreak in 2014-2016. Elastic net regularization was used to create a prognostic model for EVD mortality. In addition to external validation with data from the 2018-2020 EVD epidemic in the Democratic Republic of the Congo (DRC), we updated the model using selected serum biomarkers.
findingsPediatric EVD mortality was significantly associated with younger age, lower PCR cycle threshold (Ct) values, unexplained bleeding, respiratory distress, bone/muscle pain, anorexia, dysphagia, and diarrhea. These variables were combined to develop the newly described EVD Prognosis in Children (EPiC) predictive model. The area under the receiver operating characteristic curve (AUC) for EPiC was 0.77 (95% CI: 0.74-0.81) in the West Africa derivation dataset and 0.76 (95% CI: 0.64-0.88) in the DRC validation dataset. Updating the model with peak aspartate aminotransferase (AST) or creatinine kinase (CK) measured within the first 48 hours after admission increased the AUC to 0.90 (0.77-1.00) and 0.87 (0.74-1.00), respectively.
conclusionThe novel EPiC prognostic model that incorporates clinical information and commonly used biochemical tests, such as AST and CK, can be used to predict mortality in children with EVD.
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.