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Title: Early prediction of severe maternal morbidity using machine learning techniquesAuthor: Rodríguez E.A. / Estrada F.E. / Torres W.C. / Santos J.C.M.Language: InglésRepository: 61Subject: 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
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Title: A machine learning model for occupancy rates and demand forecasting in the hospitality industryAuthor: Caicedo-Torres W. / Payares F.Language: InglésRepository: 61Subject: Forecasting / Hotel occupancy. Demand / Kernel Ridge Regression / Machine learning / Neural Networks / Ridge regression / Artificial intelligence / Costs / Decision making / Economics / Hotels / Learning systems / Neural networks / Regression analysis / Decision making process / Financial performance / Kernel ridge regressions / Machine learning models / Machine learning techniques / Mean absolute percentage error / Specialized software
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Title: Machine learning models for early dengue severity predictionAuthor: Caicedo-Torres W. / Paternina Á. / Pinzón H.Language: InglésRepository: 61Subject: Children / Dengue / Logistic regression / Machine learning / Naive bayes / PICU / Severity / SVM / Artificial intelligence / Health risks / Intensive care units / Learning algorithms / Pediatrics / Viruses / Logistic regressions / Learning systems
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Title: A machine learning model for triage in lean pediatric emergency departmentsAuthor: Caicedo-Torres W. / García G. / Pinzón H.Language: InglésRepository: 61Subject: Emergency department / Fast track / Lean / Logistic regression / Machine learning / Neural networks / PCA / SVM / Triage / Artificial intelligence / Complex networks / Emergency rooms / Hospitals / Patient monitoring / Patient treatment / Pediatrics / Emergency departments / Fast tracks / Logistic regressions / Learning systems
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Title: Protein fold families prediction based on graph representations and machine learning methods / 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016Language: DesconocidoRepository: 56
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