Machine learning models for early dengue severity prediction
Infection by dengue-virus is prevalent and a public health issue in tropical countries worldwide. Also, in developing nations, child populations remain at risk of adverse events following an infection by dengue virus, as the necessary care is not always accessible, or health professionals are withou...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8998
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8998
- Palabra clave:
- Children
Dengue
Logistic regression
Machine learning
Naive bayes
PICU
Severity
SVM
Artificial intelligence
Health risks
Intensive care units
Learning algorithms
Pediatrics
Viruses
Children
Dengue
Logistic regressions
Naive bayes
PICU
Severity
Learning systems
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Machine learning models for early dengue severity prediction |
| title |
Machine learning models for early dengue severity prediction |
| spellingShingle |
Machine learning models for early dengue severity prediction Children Dengue Logistic regression Machine learning Naive bayes PICU Severity SVM Artificial intelligence Health risks Intensive care units Learning algorithms Pediatrics Viruses Children Dengue Logistic regressions Naive bayes PICU Severity Learning systems |
| title_short |
Machine learning models for early dengue severity prediction |
| title_full |
Machine learning models for early dengue severity prediction |
| title_fullStr |
Machine learning models for early dengue severity prediction |
| title_full_unstemmed |
Machine learning models for early dengue severity prediction |
| title_sort |
Machine learning models for early dengue severity prediction |
| dc.contributor.editor.none.fl_str_mv |
Escalante H.J. Montes-y-Gomez M. Segura A. de Dios Murillo J. |
| dc.subject.keywords.none.fl_str_mv |
Children Dengue Logistic regression Machine learning Naive bayes PICU Severity SVM Artificial intelligence Health risks Intensive care units Learning algorithms Pediatrics Viruses Children Dengue Logistic regressions Naive bayes PICU Severity Learning systems |
| topic |
Children Dengue Logistic regression Machine learning Naive bayes PICU Severity SVM Artificial intelligence Health risks Intensive care units Learning algorithms Pediatrics Viruses Children Dengue Logistic regressions Naive bayes PICU Severity Learning systems |
| description |
Infection by dengue-virus is prevalent and a public health issue in tropical countries worldwide. Also, in developing nations, child populations remain at risk of adverse events following an infection by dengue virus, as the necessary care is not always accessible, or health professionals are without means to cheaply and reliably predict how likely is for a patient to experience severe Dengue. Here, we propose a classification model based on Machine Learning techniques, which predicts whether or not a pediatric patient will be admitted into the pediatric Intensive Care Unit, as a proxy for Dengue severity. Different Machine Learning techniques were trained and validated using Stratified 5-Fold Cross-Validation, and the best model was evaluated on a disjoint test set. Cross-Validation results showed an SVM with Gaussian Kernel outperformed the other models considered, with an 0.81 Receiver Operating Characteristic Area Under the Curve (ROC AUC) score. Subsequent results over the test set showed a 0.75 ROC AUC score. Validation and test results are promising and support further research and development. © Springer International Publishing AG 2016. |
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2016 |
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2016 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:32:44Z |
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2020-03-26T16:32:44Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Conferencia |
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 247-258 |
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9783319479545 |
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03029743 |
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https://hdl.handle.net/20.500.12585/8998 |
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10.1007/978-3-319-47955-2_21 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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55782426500 57203489700 55782490400 |
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 247-258 9783319479545 03029743 10.1007/978-3-319-47955-2_21 Universidad Tecnológica de Bolívar Repositorio UTB 55782426500 57203489700 55782490400 |
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https://hdl.handle.net/20.500.12585/8998 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
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23 November 2016 through 25 November 2016 |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
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Recurso electrónico |
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Springer Verlag |
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Springer Verlag |
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Universidad Tecnológica de Bolívar |
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15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016 |
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Escalante H.J.Montes-y-Gomez M.Segura A.de Dios Murillo J.Caicedo-Torres W.Paternina Á.Pinzón H.2020-03-26T16:32:44Z2020-03-26T16:32:44Z2016Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 247-258978331947954503029743https://hdl.handle.net/20.500.12585/899810.1007/978-3-319-47955-2_21Universidad Tecnológica de BolívarRepositorio UTB557824265005720348970055782490400Infection by dengue-virus is prevalent and a public health issue in tropical countries worldwide. Also, in developing nations, child populations remain at risk of adverse events following an infection by dengue virus, as the necessary care is not always accessible, or health professionals are without means to cheaply and reliably predict how likely is for a patient to experience severe Dengue. Here, we propose a classification model based on Machine Learning techniques, which predicts whether or not a pediatric patient will be admitted into the pediatric Intensive Care Unit, as a proxy for Dengue severity. Different Machine Learning techniques were trained and validated using Stratified 5-Fold Cross-Validation, and the best model was evaluated on a disjoint test set. Cross-Validation results showed an SVM with Gaussian Kernel outperformed the other models considered, with an 0.81 Receiver Operating Characteristic Area Under the Curve (ROC AUC) score. Subsequent results over the test set showed a 0.75 ROC AUC score. Validation and test results are promising and support further research and development. © Springer International Publishing AG 2016.Recurso electrónicoapplication/pdfengSpringer Verlaghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84994130153&doi=10.1007%2f978-3-319-47955-2_21&partnerID=40&md5=65a9cbf885b81a5735f5593296b0d24415th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016Machine learning models for early dengue severity predictioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fChildrenDengueLogistic regressionMachine learningNaive bayesPICUSeveritySVMArtificial intelligenceHealth risksIntensive care unitsLearning algorithmsPediatricsVirusesChildrenDengueLogistic regressionsNaive bayesPICUSeverityLearning systems23 November 2016 through 25 November 2016Anders, K.L., Nguyet, N.M., Van Vinh Chau, N., Hung, N.T., Thuy, T.T., Lien, L.B., Farrar, J., Simmons, C.P., Epidemiological factors associated with dengue shock syndrome and mortality in hospitalized dengue patients in Ho Chi Minh City, Vietnam (2011) Am. J. Trop. Med. Hyg., 84 (1), pp. 127-134. , http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005500/Avirutnan, P., Punyadee, N., Noisakran, S., Komoltri, C., Thiemmeca, S., Auethavornanan, K., Jairungsri, A., Malasit, P., Vascular leakage in severe dengue virus infections: A potential role for the nonstructural viral protein ns1 and complement (2006) J. Infect. Dis, 193 (8), pp. 1078-1088Bayes, M., Price, M., (1763) An Essay Towards Solving a Problem in the Doctrine of Chances, 53, pp. 370-418. , http://rstl.royalsocietypublishing.org/content/53/370.short, By the Late Rev. Mr. Bayes, F.R.S. communicated by Mr. Price, in a letter to John Canton, A.M.F.R.S. Philos. TransBhatt, S., Gething, P.W., Brady, O.J., Messina, J.P., Farlow, A.W., Moyes, C.L., Drake, J.M., Hay, S.I., The global distribution and burden of dengue (2013) Nature, 496 (7446), pp. 504-507. , http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651993/Cao, X.T.P., Ngo, T.N., Wills, B., Kneen, R., Nguyen, T.T.H., Ta, T.T.M., Tran, T.T.H., Farrar, J.J., Evaluation of the world health organization standard tourniquet test and a modified tourniquet test in the diagnosis of dengue infection in Vietnam (2002) Trop. Med. Int. Health, 7 (2), pp. 125-132Carrasco, L.R., Leo, Y.S., Cook, A.R., Lee, V.J., Thein, T.L., Go, C.J., Lye, D.C., Predictive tools for severe dengue conforming to world health organization 2009 criteria (2014) Plos Negl. Trop. Dis, 8 (7). , http://dx.doi.org/10.1371Cortes, C., Vapnik, V., Support-vector networks (1995) Mach. Learn, 20 (3), pp. 273-297. , http://dx.doi.org/10.1007/BF00994018Domingos, P., Pazzani, M., On the optimality of the simple bayesian classifier under zero-one loss (1997) Mach. Learn, 29 (2-3), pp. 03-130. , http://dx.doi.org/10.1023/A:1007413511361Gomes, A.L.V., Wee, L.J.K., Khan, A.M., Gil, L.H.V.G., Marques, E.T.A.J., Calzavara-Silva, C.E., Tan, T.W., Classification of dengue fever patients based on gene expression data using support vector machines (2010) Plos One, 5 (6)Guzman, M., Alvarez, M., Halstead, S., Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: An historical perspective and role of antibody-dependent enhancement of infection (2013) Arch. Virol, 158 (7), pp. 1445-1459. , http://dx.doi.org/10.1007/s00705-013-1645-3Huy, N.T., Thao, N.T.H., Ha, T.T.N., Lan, N.T.P., Nga, P.T.T., Thuy, T.T., Tuan, H.M., Hirayama, K., Development of clinical decision rules to predict recurrent shock in dengue (2013) Crit. Care, 17 (6), pp. R280-R280. , http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4057383/Kalayanarooj, S., Nimmannitya, S., Is dengue severity related to nutritional status? (2005) SE Asian J. Trop. Med. 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Public Health, 6 (3), pp. 126-133Tanner, L., Schreiber, M., Low, J.G.H., Ong, A., Tolfvenstam, T., Lai, Y.L., Ng, L.C., Ooi, E.E., Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness (2008) Plos Negl. Trop. Dis, 2 (3). , http://dx.plos.org/10.1371Trung, D.T., Thao, L.T.T., Dung, N.M., Ngoc, T.V., Hien, T.T., Chau, N.V.V., Wolbers, M., Wills, B., Clinical features of dengue in a large vietnamese cohort: Intrinsically lower platelet counts and greater risk for bleeding in adults than children (2012) Plos Negl. Trop. Dis, 6 (6)Vaughn, D.W., Green, S., Kalayanarooj, S., Innis, B.L., Nimmannitya, S., Suntayakorn, S., Endy, T.P., Nisalak, A., Dengue viremia titer, antibody response pattern, and virus serotype correlate with disease severity (2000) J. Infect. Dis., 181 (1), pp. 2-9http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8998/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8998oai:repositorio.utb.edu.co:20.500.12585/89982021-02-01 20:05:10.451Repositorio Institucional UTBrepositorioutb@utb.edu.co |