Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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
_version_ 1785077993857613824
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
work_keys_str_mv AT vudavidm detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT krystosikamyr detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT ndengabrysona detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT mutukufrancism detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT rippkelsey detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT liuelizabeth detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT bosirecarrenm detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT heathclaire detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT chebiiphilip detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT mainapriscillawatiri detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT jembezainab detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT malumbosaidlipi detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT amugongojaelsagina detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT rongacharles detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT okutavictoria detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT mutainoah detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT makenzinzarog detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT litundakennedya detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT mukokodunstan detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT kingcharlesh detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms
AT labeaudadesiree detectionofacutedenguevirusinfectionwithandwithoutconcurrentmalariainfectioninacohortoffebrilechildreninkenya20142019bycliniciansormachinelearningalgorithms