Philosophical Considerations in Health: Conceptualizing to Educate—A Perspective on Neglected Tropical Diseases in Brazil

Santos, Dilvani Oliveira; Faria, Ludmila Veiga; Nascimento, Anna Fernandes Silva Chagas do

doi:10.4236/ce.2019.106085

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…In addition to the implementation of an algorithm that helps choose the correct therapy, the use of intelligent data collection devices would allow for higher quality and validation [36]. Such devices could be used to guarantee higher-quality data for future research and innovations regarding the improvement of health services as a whole, since poor-quality data is not limited to leprosy [37]. An algorithm is nothing more than a finite sequence of well-defined instructions that can be automated, usually to solve problems.…”

Section: Relevance Of Using Aimentioning

confidence: 99%

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App

Souza

Lopes

Branco³

et al. 2021

JMIR Mhealth Uhealth

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Background According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. Objective The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). Methods Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. Results We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was “operational classification does not match the clinical form.” After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. Conclusions The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.

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Section: Relevance Of Using Aimentioning

confidence: 99%

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App

Souza

Lopes

Branco³

et al. 2021

JMIR Mhealth Uhealth

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“…Pour Graciano-Machuga et al(23) parmi les étudiants multidisciplinaires (sciences humaines, sciences de santé, sciences exactes, sciences agricoles, sciences biologiques, administration et art), les connaissances sur la lèpre étaient insuffisantes et dans la majorité des cas les attitudes défavorables envers les lépreux ont été constatées. L'actualisation des connaissances sur la lèpre permet de démystifier la stigmatisation, qui agit négativement sur les mesures de lutte(9). L'objectif de zéro discrimination contenu dans le plan 201-2030 de l'OMS met l'accent sur la « formation à la lèpre dans le programme d'enseignement médical » (27) car la transmission des connaissances demeure essentielle pour lutter contre l'ignorance et ainsi éviter la discrimination des malades.…”

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“…De ce fait, toutes les pratiques tendant à éviter l'adhésion des communautés où sévit la maladie et des malades à la détection et traitement sont à décourager (4-7). Dans la littérature scientifique, des cas de stigmatisation et discrimination des lépreux ont été évoqués(6,8,9). Aucun pays n'est à l' abri, alors que les attitudes négatives ne favorisent pas d'atteindre les objectifs de la lutte anti-lépreuse préconisés par l'organisation mondiale de la santé (OMS)(10,11).…”

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Connaissances Et Attitudes Des Etudiants en Sciences Medicales Sur La Lepre Dans La Ville De Kalemie, Republique Democratique Du Congo

Héman,

Sylvain,

Ernest

et al. 2023

Tanganyika J. Sc.

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Introduction : La lèpre est une maladie tropicale négligée causée par les Mycobacteri leprae et lepromatosis. Elle est endémique dans la province du Tanganyika où elle demeure encore un problème de santé publique. La stigmatisation et la discrimination envers les lépreux découlent du niveau de connaissance de la population sur la lèpre. Cette première étude en milieu académique congolais a été menée dans l’objectif d’évaluer les connaissances et l’attitude des étudiants en sciences médicales au regard de la lèpre. Méthodologie : Il s’agissait d’une étude analytique transversale réalisée au moyen de questionnaire fermé, parmi les étudiants de l’Institut Supérieur des Techniques médicales de Kalemie de Septembre à Décembre 2022. Les données collectées ont été analysées à l’aide du logiciel Epi-Info 7.2. Le seuil de significativité de test était fixé à p < 0,05. Résultats : Sur 299 étudiants interrogés, 98,6% avaient déjà entendu parler de la lèpre, dont 52,2% lors des séances des cours. L’âge moyen des enquêtés était de 24,7 ± 4,7 ans, avec une légère prédominance féminine (50,2%). La majorité des étudiants étaient en sciences infirmières (74,9%). Le niveau de connaissance était bon à 35,9% et l’attitude était positive chez 12,5% d’enquêtés. La bonne connaissance sur la lèpre était significativement associée au sexe féminin (OR = 1,8 [IC95% : 1,1-2,9] ; p=0,023), au statut matrimonial autre que célibataire ( OR=2,834 [IC95% : 1,581-5,079] ; p=0,006]), et à la principale source d’information constituée des enseignements des cours (OR=8,3 [IC95% : 4,6-14,8] ; p<0,0001). Les enseignements aux cours, la résidence en milieu urbain, la volonté de partager à manger avec le lépreux et autoriser son enfant à jouer avec le lépreux sont associés à l’attitude positive des étudiants envers les lépreux. Conclusion : En milieu de forte incidence de la lèpre, les résultats de cette étude témoignent de la nécessité de mis en place des stratégies de sensibilisation et d’éducation des étudiants sur la lèpre pour lutter contre l’ignorance et la stigmatisation. Mots-clés : Lèpre, Maladie de Hansen, Stigmatisation, Kalemie, RDC

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Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App (Preprint)

Souza¹,

Lopes²,

Branco³

et al. 2020

Preprint

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BACKGROUND According to WHO, to achieve targets for control of leprosy by 2030, it will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden over the decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. OBJECTIVE The objective of this study is to assess the quality of leprosy data in Brazil by SINAN (Information System for Notifiable Diseases) and build a web application to increase the accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from the country's major diagnostic centers. METHODS Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build artificial intelligence (AI) decision model based on Random Forest (RF) algorithm to predict operational classification in Paucibacillary (PB) or Multibacillary (MB). It used the software: i) Python to extract and clean the data; ii) R to train and test the AI model via cross-validation. To allow broad access, we deployed the final RF classification model in a web application that integrates the cloud service, Microsoft Azure, with a friendly layout built in Bubble.io. It used data available on the IBGE (Brazilian Institute of Geography and Statistics) and the DATASUS (Department of Informatics of the Unified Health System). RESULTS We mapped the dispersion of leprosy incidence in Brazil, 2014 to 2018, and noticed a high number of cases in central Brazil in 2014 that became even higher in 2018, in the state of Mato Grosso. Some municipalities showed discrepancies in the 80% range. We considered inconsistency the fact of not matching a set of standards for leprosy classification, according to WHO. Of a total of 21,047 discrepancies detected, the main was considered the operational rating that does not match the clinical form. After data processing, we identified a total of 77,628 cases with missing data. Regarding the quality of the AI model applied, the sensitivity was 93.97%, and the specificity was 87.09%. In most states of Brazil, human and machine confidence intervals intersect. CONCLUSIONS The proposed APP was able to recognize patterns in leprosy cases registered in the SINAN database and classify new patients as PB or MB, reducing the probability of oversight by health professionals. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI model implemented in this work presented relatively large confidence intervals of accuracy that varied from across Brazilian states. This distortion is possibly due to the quality of the data that fed the classification model.

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Philosophical Considerations in Health: Conceptualizing to Educate—A Perspective on Neglected Tropical Diseases in Brazil

Cited by 3 publications

References 16 publications

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App

Connaissances Et Attitudes Des Etudiants en Sciences Medicales Sur La Lepre Dans La Ville De Kalemie, Republique Democratique Du Congo

Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App (Preprint)

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