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Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children

It is currently estimated that 67% of malaria deaths occur in children under-five years (WHO, 2020). To improve the identification of children at clinical risk for malaria, the WHO developed community (iCCM) and clinic-based (IMCI) protocols for frontline health workers using paper-based forms or di...

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Autores principales: McLaughlin, Megan, Pellé, Karell G., Scarpino, Samuel V., Giwa, Aisha, Mount-Finette, Ezra, Haidar, Nada, Adamu, Fatima, Ravi, Nirmal, Thompson, Adam, Heath, Barry, Dittrich, Sabine, Finette, Barry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851346/
https://www.ncbi.nlm.nih.gov/pubmed/35187469
http://dx.doi.org/10.3389/frai.2021.554017
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author McLaughlin, Megan
Pellé, Karell G.
Scarpino, Samuel V.
Giwa, Aisha
Mount-Finette, Ezra
Haidar, Nada
Adamu, Fatima
Ravi, Nirmal
Thompson, Adam
Heath, Barry
Dittrich, Sabine
Finette, Barry
author_facet McLaughlin, Megan
Pellé, Karell G.
Scarpino, Samuel V.
Giwa, Aisha
Mount-Finette, Ezra
Haidar, Nada
Adamu, Fatima
Ravi, Nirmal
Thompson, Adam
Heath, Barry
Dittrich, Sabine
Finette, Barry
author_sort McLaughlin, Megan
collection PubMed
description It is currently estimated that 67% of malaria deaths occur in children under-five years (WHO, 2020). To improve the identification of children at clinical risk for malaria, the WHO developed community (iCCM) and clinic-based (IMCI) protocols for frontline health workers using paper-based forms or digital mobile health (mHealth) platforms. To investigate improving the accuracy of these point-of-care clinical risk assessment protocols for malaria in febrile children, we embedded a malaria rapid diagnostic test (mRDT) workflow into THINKMD’s (IMCI) mHealth clinical risk assessment platform. This allowed us to perform a comparative analysis of THINKMD-generated malaria risk assessments with mRDT truth data to guide modification of THINKMD algorithms, as well as develop new supervised machine learning (ML) malaria risk algorithms. We utilized paired clinical data and malaria risk assessments acquired from over 555 children presenting to five health clinics in Kano, Nigeria to train ML algorithms to identify malaria cases using symptom and location data, as well as confirmatory mRDT results. Supervised ML random forest algorithms were generated using 80% of our field-based data as the ML training set and 20% to test our new ML logic. New ML-based malaria algorithms showed an increased sensitivity and specificity of 60 and 79%, and PPV and NPV of 76 and 65%, respectively over THINKD initial IMCI-based algorithms. These results demonstrate that combining mRDT “truth” data with digital mHealth platform clinical assessments and clinical data can improve identification of children with malaria/non-malaria attributable febrile illnesses.
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spelling pubmed-88513462022-02-18 Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children McLaughlin, Megan Pellé, Karell G. Scarpino, Samuel V. Giwa, Aisha Mount-Finette, Ezra Haidar, Nada Adamu, Fatima Ravi, Nirmal Thompson, Adam Heath, Barry Dittrich, Sabine Finette, Barry Front Artif Intell Artificial Intelligence It is currently estimated that 67% of malaria deaths occur in children under-five years (WHO, 2020). To improve the identification of children at clinical risk for malaria, the WHO developed community (iCCM) and clinic-based (IMCI) protocols for frontline health workers using paper-based forms or digital mobile health (mHealth) platforms. To investigate improving the accuracy of these point-of-care clinical risk assessment protocols for malaria in febrile children, we embedded a malaria rapid diagnostic test (mRDT) workflow into THINKMD’s (IMCI) mHealth clinical risk assessment platform. This allowed us to perform a comparative analysis of THINKMD-generated malaria risk assessments with mRDT truth data to guide modification of THINKMD algorithms, as well as develop new supervised machine learning (ML) malaria risk algorithms. We utilized paired clinical data and malaria risk assessments acquired from over 555 children presenting to five health clinics in Kano, Nigeria to train ML algorithms to identify malaria cases using symptom and location data, as well as confirmatory mRDT results. Supervised ML random forest algorithms were generated using 80% of our field-based data as the ML training set and 20% to test our new ML logic. New ML-based malaria algorithms showed an increased sensitivity and specificity of 60 and 79%, and PPV and NPV of 76 and 65%, respectively over THINKD initial IMCI-based algorithms. These results demonstrate that combining mRDT “truth” data with digital mHealth platform clinical assessments and clinical data can improve identification of children with malaria/non-malaria attributable febrile illnesses. Frontiers Media S.A. 2022-02-03 /pmc/articles/PMC8851346/ /pubmed/35187469 http://dx.doi.org/10.3389/frai.2021.554017 Text en Copyright © 2022 McLaughlin, Pellé, Scarpino, Giwa, Mount-Finette, Haidar, Adamu, Ravi, Thompson, Heath, Dittrich and Finette. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
McLaughlin, Megan
Pellé, Karell G.
Scarpino, Samuel V.
Giwa, Aisha
Mount-Finette, Ezra
Haidar, Nada
Adamu, Fatima
Ravi, Nirmal
Thompson, Adam
Heath, Barry
Dittrich, Sabine
Finette, Barry
Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children
title Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children
title_full Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children
title_fullStr Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children
title_full_unstemmed Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children
title_short Development and Validation of Manually Modified and Supervised Machine Learning Clinical Assessment Algorithms for Malaria in Nigerian Children
title_sort development and validation of manually modified and supervised machine learning clinical assessment algorithms for malaria in nigerian children
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851346/
https://www.ncbi.nlm.nih.gov/pubmed/35187469
http://dx.doi.org/10.3389/frai.2021.554017
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