Early prediction of severe maternal morbidity using machine learning techniques
Severe Maternal Morbidity is a public health issue. It may occur during pregnancy, delivery, or puerperium due to conditions (hypertensive disorders, hemorrhages, infections and others) that put in risk the women’s or baby’s life. These conditions are really difficult to detect at an early stage. In...
- 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:20.500.12585/8999
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8999
- Palabra clave:
- Logistic regression
Machine learning
Severe maternal morbidity
Artificial intelligence
Diseases
Learning algorithms
Obstetrics
Early prediction
Learning models
Logistic regressions
Machine learning techniques
Maternal morbidity
Methods and materials
Pregnant woman
Public health issues
Learning systems
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Early prediction of severe maternal morbidity using machine learning techniques |
| title |
Early prediction of severe maternal morbidity using machine learning techniques |
| spellingShingle |
Early prediction of severe maternal morbidity using machine learning techniques Logistic regression Machine learning Severe maternal morbidity Artificial intelligence Diseases Learning algorithms Obstetrics Early prediction Learning models Logistic regressions Machine learning techniques Maternal morbidity Methods and materials Pregnant woman Public health issues Learning systems |
| title_short |
Early prediction of severe maternal morbidity using machine learning techniques |
| title_full |
Early prediction of severe maternal morbidity using machine learning techniques |
| title_fullStr |
Early prediction of severe maternal morbidity using machine learning techniques |
| title_full_unstemmed |
Early prediction of severe maternal morbidity using machine learning techniques |
| title_sort |
Early prediction of severe maternal morbidity using machine learning techniques |
| 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 |
Logistic regression Machine learning Severe maternal morbidity Artificial intelligence Diseases Learning algorithms Obstetrics Early prediction Learning models Logistic regressions Machine learning techniques Maternal morbidity Methods and materials Pregnant woman Public health issues Learning systems |
| topic |
Logistic regression Machine learning Severe maternal morbidity Artificial intelligence Diseases Learning algorithms Obstetrics Early prediction Learning models Logistic regressions Machine learning techniques Maternal morbidity Methods and materials Pregnant woman Public health issues Learning systems |
| description |
Severe Maternal Morbidity is a public health issue. It may occur during pregnancy, delivery, or puerperium due to conditions (hypertensive disorders, hemorrhages, infections and others) that put in risk the women’s or baby’s life. These conditions are really difficult to detect at an early stage. In response to the above, this work proposes using several machine learning techniques, which are considered most relevant in a bio-medical setting, in order to predict the risk level for Severe Maternal Morbidity in patients during pregnancy. The population studied correspond to pregnant women receiving prenatal care and final attention at E.S.E Clínica de Maternidad Rafael Calvo in Cartagena, Colombia. This paper presents the preliminary results of an ongoing project, as well as methods and materials considered for the construction of the learning models. © Springer International Publishing AG 2016. |
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2016 |
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2016 |
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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. 259-270 |
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9783319479545 |
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03029743 |
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https://hdl.handle.net/20.500.12585/8999 |
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10.1007/978-3-319-47955-2_22 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57203489577 57191835839 57191844192 26325154200 |
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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. 259-270 9783319479545 03029743 10.1007/978-3-319-47955-2_22 Universidad Tecnológica de Bolívar Repositorio UTB 57203489577 57191835839 57191844192 26325154200 |
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https://hdl.handle.net/20.500.12585/8999 |
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eng |
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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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Recurso electrónico |
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Springer Verlag |
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Springer Verlag |
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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.Rodríguez E.A.Estrada F.E.Torres W.C.Santos J.C.M.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. 259-270978331947954503029743https://hdl.handle.net/20.500.12585/899910.1007/978-3-319-47955-2_22Universidad Tecnológica de BolívarRepositorio UTB57203489577571918358395719184419226325154200Severe Maternal Morbidity is a public health issue. It may occur during pregnancy, delivery, or puerperium due to conditions (hypertensive disorders, hemorrhages, infections and others) that put in risk the women’s or baby’s life. These conditions are really difficult to detect at an early stage. In response to the above, this work proposes using several machine learning techniques, which are considered most relevant in a bio-medical setting, in order to predict the risk level for Severe Maternal Morbidity in patients during pregnancy. The population studied correspond to pregnant women receiving prenatal care and final attention at E.S.E Clínica de Maternidad Rafael Calvo in Cartagena, Colombia. This paper presents the preliminary results of an ongoing project, as well as methods and materials considered for the construction of the learning models. © 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-s2.0-84994065492&doi=10.1007%2f978-3-319-47955-2_22&partnerID=40&md5=b77298054334f8966266596a659625f015th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016Early prediction of severe maternal morbidity using machine learning techniquesinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fLogistic regressionMachine learningSevere maternal morbidityArtificial intelligenceDiseasesLearning algorithmsObstetricsEarly predictionLearning modelsLogistic regressionsMachine learning techniquesMaternal morbidityMethods and materialsPregnant womanPublic health issuesLearning systems23 November 2016 through 25 November 2016Carty, D.M., Siwy, J., Brennand, J.E., Zürbig, P., Mullen, W., Franke, J., McCulloch, J.W., Mischak, H., Urinary proteomics for prediction of preeclampsia (2011) Hypertension, 57 (3), pp. 561-569Casal, J., Mateu, E., Tipos de muestreo (2003) Rev. Epidem. Med. Prev, 1 (1), pp. 3-7Duran, M.E.M., García, O.E.P., Carey, A.C., Bonilla, H.Q., Espitia, N.C.C., Barros, E.C., Protocolo De Vigilancia En Salud PFarran, B., Channanath, A.M., Behbehani, K., Thanaraj, T.A., Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: Machine-learning algorithms and validation using national health data from kuwaita cohort study (2013) BMJ Open, 3 (5)Haaga, J.G., Wasserheit, J.N., Tsui, A.O., (1997) Reproductive Health in Developing Countries: Expanding Dimensions, Building Solutions, , National Academies Press, Washington, D.CMariño Martínez, C.A., Fiesco, V., Carolina, D., Caracterizaci, , Ph.D. thesis, Universidad Nacional de ColombiaMorales-Osorno, B., Martínez, D.M., Cifuentes-Borrero, R., Extreme maternal morbidity in Clinica Rafael Uribe Uribe, Cali, Colombia, from January 2003 to May (2007) Revista Colombiana De Obstetricia Y Ginecolog, 58 (3), pp. 184-188Nanda, S., Savvidou, M., Syngelaki, A., Akolekar, R., Nicolaides, K.H., Prediction of gestational diabetes mellitus by maternal factors and biomarkers at 11 to 13 weeks (2011) Prenat. Diagn, 31 (2), pp. 135-141Neocleous, C.K., Anastasopoulos, P., Nikolaides, K.H., Schizas, C.N., Neokleous, K.C., Neural networks to estimate the risk for preeclampsia occurrence (2009) International Joint Conference on Neural Networks, IJCNN 2009, pp. 2221-2225. , IEEE(1996), Revised 1990 estimates of maternal mortality: a new approach. World Health OrganizationPark, F.J., Leung, C.H., Poon, L.C., Williams, P.F., Rothwell, S.J., Hyett, J.A., Clinical evaluation of a first trimester algorithm predicting the risk of hypertensive disease of pregnancy (2013) Aust. N. Z. J. Obstet. Gynaecol, 53 (6), pp. 532-539Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in Python (2011) J. Mach. Learn. Res, 12, pp. 2825-2830Poon, L.C., Kametas, N.A., Maiz, N., Akolekar, R., Nicolaides, K.H., Firsttrimester prediction of hypertensive disorders in pregnancy (2009) Hypertension, 53 (5), pp. 812-818Rojas, J.A., Cogollo, M., Miranda, J.E., Ramos, E.C., Fernández, J.C., Bello, A.M., Morbilidad materna extrema en cuidados intensivos obst Revista Colombiana De Obstetricia Y Ginecologde la Salud, O.P., (1995) Clasificación estadística Internacional De Enfermedades Y Problemas Relacionados Con La Salud: Décima revisi´on: CIE-10, , Pan American Health Orgde Vigilancia, S., Control en salud p (2012) Informe De Intoxicaciones Por Plaguicidas, , Instituto Nacional de Salud, INS. Bogot, á, Colombiahttp://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8999/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8999oai:repositorio.utb.edu.co:20.500.12585/89992021-02-02 13:53:54.925Repositorio Institucional UTBrepositorioutb@utb.edu.co |