Machine-learning Prognostic Models from the 2014–16 Ebola Outbreak: Data-harmonization Challenges, Validation Strategies, and mHealth Applications
Open Access
- 1 May 2019
- journal article
- research article
- Published by Elsevier BV in EClinicalMedicine
- Vol. 11, 54-64
- https://doi.org/10.1016/j.eclinm.2019.06.003
Abstract
No abstract availableKeywords
Funding Information
- Howard Hughes Medical Institute (R01AI114855, U01HG007480, U19AI110818)
- Bill & Melinda Gates Foundation (OPP1195122)
- NIH (U19AI115589)
- NIH (U19AI135995)
- NIH (HHSN272201400048C)
This publication has 22 references indexed in Scilit:
- Multicolored silver nanoparticles for multiplexed disease diagnostics: distinguishing dengue, yellow fever, and Ebola virusesLab on a Chip, 2015
- IntroductionSpringer Series in Statistics, 2015
- Clinical Illness and Outcomes in Patients with Ebola in Sierra LeoneThe New England Journal of Medicine, 2014
- Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreakScience, 2014
- Using electronic technology to improve clinical care – results from a before-after cluster trial to evaluate assessment and classification of sick children according to Integrated Management of Childhood Illness (IMCI) protocol in TanzaniaBMC Medical Informatics and Decision Making, 2013
- Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled studyBMJ Quality & Safety, 2013
- Missing covariate data in medical research: To impute is better than to ignoreJournal of Clinical Epidemiology, 2010
- Comprehensive Panel of Real-Time TaqMan™ Polymerase Chain Reaction Assays for Detection and Absolute Quantification of Filoviruses, Arenaviruses, and New World HantavirusesThe American Journal of Tropical Medicine and Hygiene, 2010
- The estimation ofR2and adjustedR2in incomplete data sets using multiple imputationJournal of Applied Statistics, 2009
- What's the Relative Risk?JAMA, 1998