ArticleEClinicalMedicine
Machine-learning Prognostic Models from the 2014-16 Ebola Outbreak: Data-harmonization Challenges, Validation Strategies, and mHealth Applications.
Article in EClinicalMedicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Identification of Host Gene Transcripts by Machine Learning and Their Application to Predict Outcome in Ebola Virus Disease.The Journal of infectious diseases · 2026Article
- AI-Enhanced Point-of-Care Diagnostics for Infectious Diseases in Resource-Limited Settings: A Scoping Review.Tropical medicine & international health : TM & IH · 2026Article
- Feasibility of AI-driven disease surveillance systems at international airports in sub-Saharan Africa: a narrative review.Frontiers in public health · 2026Review
- Refining early detection of Marburg Virus Disease (MVD) in Rwanda: Leveraging predictive symptom clusters to enhance case definitions.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 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
- Ladybug Beetle Optimization algorithm: application for real-world problems.The Journal of supercomputing · 2023Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Constructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Disease.PLoS neglected tropical diseases · 2022Article
- Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan.Socio-economic planning sciences · 2022Article
- 12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context.Frontiers in digital health · 2022Review
- Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.PloS one · 2022Article
- Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network.IEEE access : practical innovations, open solutions · 2022Article
- Economic impact of a machine learning-based strategy for preparation of blood products in brain tumor surgery.PloS one · 2022Article
- An intelligent framework to predict socioeconomic impacts of COVID-19 and public sentiments.Computers & electrical engineering : an international journal · 2021Article
- Comparative study of machine learning methods for COVID-19 transmission forecasting.Journal of biomedical informatics · 2021Article
- COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.Interdisciplinary sciences, computational life sciences · 2021Review
- Is There a Place for Responsible Artificial Intelligence in Pandemics? A Tale of Two Countries.Information systems frontiers : a journal of research and innovation · 2021Article
- Targeting ebola virus VP40 protein through novel inhibitors: exploring the structural and dynamic perspectives on molecular landscapes.Journal of molecular modeling · 2021Article
- Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.Frontiers in digital health · 2021Review
- Digital Technology-Based Telemedicine for the COVID-19 Pandemic.Frontiers in medicine · 2021Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundEbola virus disease (EVD) plagues low-resource and difficult-to-access settings. Machine learning prognostic models and mHealth tools could improve the understanding and use of evidence-based care guidelines in such settings. However, data incompleteness and lack of interoperability limit model generalizability. This study harmonizes diverse datasets from the 2014-16 EVD epidemic and generates several prognostic models incorporated into the novel
methodsMultivariate logistic regression was applied to investigate survival outcomes in 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone at various timepoints during 2014-16. We generated a parsimonious model (viral load, age, temperature, bleeding, jaundice, dyspnea, dysphagia, and time-to-presentation) and several fallback models for when these variables are unavailable. All were externally validated against two independent datasets and compared to further models including expert observational wellness assessments. Models were incorporated into an app highlighting the signs/symptoms with the largest contribution to prognosis.
findingsThe parsimonious model approached the predictive power of observational assessments by experienced clinicians (Area-Under-the-Curve, AUC = 0.70-0.79, accuracy = 0.64-0.74) and maintained its performance across subcohorts with different healthcare seeking behaviors. Age and viral load contributed > 5-fold the weighting of other features and including them in a minimal model had a similar AUC, albeit at the cost of specificity.
interpretationClinically guided prognostic models can recapitulate clinical expertise and be useful when such expertise is unavailable. Incorporating these models into mHealth tools may facilitate their interpretation and provide informed access to comprehensive clinical guidelines.
fundingHoward Hughes Medical Institute, US National Institutes of Health, Bill & Melinda Gates Foundation, International Medical Corps, UK Department for International Development, and GOAL Global.
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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.