Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: asystematic review
| العنوان: | Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: asystematic review |
|---|---|
| المؤلفون: | Sebastião Rogério da Silva Neto, Thomás Tabosa Oliveira, Igor Vitor Teixeira, Samuel Benjamin Aguiar de Oliveira, Vanderson Souza Sampaio, Theo Lynn, Patricia Takako Endo |
| المصدر: | Rogério da Silva Neto, Sebastião ORCID: 0000-0001-8109-697X <https://orcid.org/0000-0001-8109-697X>, Oliveira, Thomás Tabosa ORCID: 0000-0001-8224-5922 <https://orcid.org/0000-0001-8224-5922>, Teixeira, Igor Vitor, Aguiar de Oliveira, Samuel Benjamin ORCID: 0000-0002-4821-8100 <https://orcid.org/0000-0002-4821-8100>, Sampaio, Vanderson Souza ORCID: 0000-0001-7307-8851 <https://orcid.org/0000-0001-7307-8851>, Lynn, Theo ORCID: 0000-0001-9284-7580 <https://orcid.org/0000-0001-9284-7580> and Endo, Patricia Takako ORCID: 0000-0002-9163-5583 <https://orcid.org/0000-0002-9163-5583> (2022) Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: asystematic review. PLOS Neglected Tropical Diseases, 16 (1). ISSN 1935-2735 PLoS Neglected Tropical Diseases, Vol 16, Iss 1, p e0010061 (2022) PLoS Neglected Tropical Diseases |
| بيانات النشر: | Public Library of Science (PLoS), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Viral Diseases, Computer and Information Sciences, Decision Analysis, RC955-962, Mosquito Vectors, Arbovirus Infections, Research and Analysis Methods, Dengue Fever, Dengue, Machine Learning, Deep Learning, Medical Conditions, Aedes, Artificial Intelligence, Arctic medicine. Tropical medicine, Machine learning, Medicine and Health Sciences, Animals, Humans, Zika Virus Infection, Decision Trees, Organisms, Public Health, Environmental and Occupational Health, Neglected Diseases, Biology and Life Sciences, Chikungunya Infection, Zika Virus, Dengue Virus, South America, Decision Support Systems, Clinical, Tropical Diseases, Zika Fever, Decision Tree Learning, Infectious Diseases, Arboviral Infections, Viruses, Chikungunya Fever, Engineering and Technology, Public aspects of medicine, RA1-1270, Chikungunya virus, Management Engineering, Arboviruses, Research Article, Neglected Tropical Diseases |
| الوصف: | Background Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life. Author summary Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms and, sometimes, inaccurate test results. In this paper, we present the state of the art of studies investigating the automatic classification of arboviral diseases based on Machine Learning (ML) and Deep Learning (DL) models. Results show that current research is focused on the classification of Dengue, primarily using tree-based ML algorithms. The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3c3a4eebdca9aadf679ddc6b77cee51 http://doras.dcu.ie/27528/ |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....c3c3a4eebdca9aadf679ddc6b77cee51 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |