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Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms
Poor access to diagnostic testing in resource limited settings restricts surveillance for emerging infections, such as dengue virus (DENV), to clinician suspicion, based on history and exam observations alone. We investigated the ability of machine learning to detect DENV based solely on data availa...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370704/ https://www.ncbi.nlm.nih.gov/pubmed/37494331 http://dx.doi.org/10.1371/journal.pgph.0001950 |
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author | Vu, David M. Krystosik, Amy R. Ndenga, Bryson A. Mutuku, Francis M. Ripp, Kelsey Liu, Elizabeth Bosire, Carren M. Heath, Claire Chebii, Philip Maina, Priscilla Watiri Jembe, Zainab Malumbo, Said Lipi Amugongo, Jael Sagina Ronga, Charles Okuta, Victoria Mutai, Noah Makenzi, Nzaro G. Litunda, Kennedy A. Mukoko, Dunstan King, Charles H. LaBeaud, A. Desiree |
author_facet | Vu, David M. Krystosik, Amy R. Ndenga, Bryson A. Mutuku, Francis M. Ripp, Kelsey Liu, Elizabeth Bosire, Carren M. Heath, Claire Chebii, Philip Maina, Priscilla Watiri Jembe, Zainab Malumbo, Said Lipi Amugongo, Jael Sagina Ronga, Charles Okuta, Victoria Mutai, Noah Makenzi, Nzaro G. Litunda, Kennedy A. Mukoko, Dunstan King, Charles H. LaBeaud, A. Desiree |
author_sort | Vu, David M. |
collection | PubMed |
description | Poor access to diagnostic testing in resource limited settings restricts surveillance for emerging infections, such as dengue virus (DENV), to clinician suspicion, based on history and exam observations alone. We investigated the ability of machine learning to detect DENV based solely on data available at the clinic visit. We extracted symptom and physical exam data from 6,208 pediatric febrile illness visits to Kenyan public health clinics from 2014–2019 and created a dataset with 113 clinical features. Malaria testing was available at the clinic site. DENV testing was performed afterwards. We randomly sampled 70% of the dataset to develop DENV and malaria prediction models using boosted logistic regression, decision trees and random forests, support vector machines, naïve Bayes, and neural networks with 10-fold cross validation, tuned to maximize accuracy. 30% of the dataset was reserved to validate the models. 485 subjects (7.8%) had DENV, and 3,145 subjects (50.7%) had malaria. 220 (3.5%) subjects had co-infection with both DENV and malaria. In the validation dataset, clinician accuracy for diagnosis of malaria was high (82% accuracy, 85% sensitivity, 80% specificity). Accuracy of the models for predicting malaria diagnosis ranged from 53–69% (35–94% sensitivity, 11–80% specificity). In contrast, clinicians detected only 21 of 145 cases of DENV (80% accuracy, 14% sensitivity, 85% specificity). Of the six models, only logistic regression identified any DENV case (8 cases, 91% accuracy, 5.5% sensitivity, 98% specificity). Without diagnostic testing, interpretation of clinical findings by humans or machines cannot detect DENV at 8% prevalence. Access to point-of-care diagnostic tests must be prioritized to address global inequities in emerging infections surveillance. |
format | Online Article Text |
id | pubmed-10370704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103707042023-07-27 Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms Vu, David M. Krystosik, Amy R. Ndenga, Bryson A. Mutuku, Francis M. Ripp, Kelsey Liu, Elizabeth Bosire, Carren M. Heath, Claire Chebii, Philip Maina, Priscilla Watiri Jembe, Zainab Malumbo, Said Lipi Amugongo, Jael Sagina Ronga, Charles Okuta, Victoria Mutai, Noah Makenzi, Nzaro G. Litunda, Kennedy A. Mukoko, Dunstan King, Charles H. LaBeaud, A. Desiree PLOS Glob Public Health Research Article Poor access to diagnostic testing in resource limited settings restricts surveillance for emerging infections, such as dengue virus (DENV), to clinician suspicion, based on history and exam observations alone. We investigated the ability of machine learning to detect DENV based solely on data available at the clinic visit. We extracted symptom and physical exam data from 6,208 pediatric febrile illness visits to Kenyan public health clinics from 2014–2019 and created a dataset with 113 clinical features. Malaria testing was available at the clinic site. DENV testing was performed afterwards. We randomly sampled 70% of the dataset to develop DENV and malaria prediction models using boosted logistic regression, decision trees and random forests, support vector machines, naïve Bayes, and neural networks with 10-fold cross validation, tuned to maximize accuracy. 30% of the dataset was reserved to validate the models. 485 subjects (7.8%) had DENV, and 3,145 subjects (50.7%) had malaria. 220 (3.5%) subjects had co-infection with both DENV and malaria. In the validation dataset, clinician accuracy for diagnosis of malaria was high (82% accuracy, 85% sensitivity, 80% specificity). Accuracy of the models for predicting malaria diagnosis ranged from 53–69% (35–94% sensitivity, 11–80% specificity). In contrast, clinicians detected only 21 of 145 cases of DENV (80% accuracy, 14% sensitivity, 85% specificity). Of the six models, only logistic regression identified any DENV case (8 cases, 91% accuracy, 5.5% sensitivity, 98% specificity). Without diagnostic testing, interpretation of clinical findings by humans or machines cannot detect DENV at 8% prevalence. Access to point-of-care diagnostic tests must be prioritized to address global inequities in emerging infections surveillance. Public Library of Science 2023-07-26 /pmc/articles/PMC10370704/ /pubmed/37494331 http://dx.doi.org/10.1371/journal.pgph.0001950 Text en © 2023 Vu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Vu, David M. Krystosik, Amy R. Ndenga, Bryson A. Mutuku, Francis M. Ripp, Kelsey Liu, Elizabeth Bosire, Carren M. Heath, Claire Chebii, Philip Maina, Priscilla Watiri Jembe, Zainab Malumbo, Said Lipi Amugongo, Jael Sagina Ronga, Charles Okuta, Victoria Mutai, Noah Makenzi, Nzaro G. Litunda, Kennedy A. Mukoko, Dunstan King, Charles H. LaBeaud, A. Desiree Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms |
title | Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms |
title_full | Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms |
title_fullStr | Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms |
title_full_unstemmed | Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms |
title_short | Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014–2019, by clinicians or machine learning algorithms |
title_sort | detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in kenya, 2014–2019, by clinicians or machine learning algorithms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370704/ https://www.ncbi.nlm.nih.gov/pubmed/37494331 http://dx.doi.org/10.1371/journal.pgph.0001950 |
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