A Predictive Analytic Model for Maternal Morbidity
This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on art...
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| Autores principales: | , , , , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881700450306097152 |
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| author | Edgardo Palza V Jorge Sanchez Guillermo Mamani Percy Pacora Alain Abran Jane Dominique Moon |
| author_browse | Alain Abran Edgardo Palza V Guillermo Mamani Jane Dominique Moon Jorge Sanchez Percy Pacora |
| author_facet | Edgardo Palza V Jorge Sanchez Guillermo Mamani Percy Pacora Alain Abran Jane Dominique Moon |
| author_sort | Edgardo Palza V |
| collection | Producción científica |
| description | This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Influence Diagrams and principles of traditional statistics. The model research is based on a repository of 10,000 medical records at a hospital in Peru. The model aims to identify the factors that are causes of morbidity in newborns, is based on data mining techniques and developed using the CRISP-DM methodology. |
| format | Book chapter |
| id | openalex:W4229816163 |
| institution | UPeU |
| language | English |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| record_format | dspace |
| spelling | openalex:W4229816163A Predictive Analytic Model for Maternal MorbidityEdgardo Palza VJorge SanchezGuillermo MamaniPercy PacoraAlain AbranJane Dominique Moonneonatal morbiditymaternal morbiditypredictive analyticsBayesian networksinfluence diagramsrisk factorsnewborn healthmedical recordsdata miningforecasting modelsArtificial Intelligence in HealthcareMaternal and Neonatal HealthcareData Mining and Machine Learning ApplicationsThis chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Influence Diagrams and principles of traditional statistics. The model research is based on a repository of 10,000 medical records at a hospital in Peru. The model aims to identify the factors that are causes of morbidity in newborns, is based on data mining techniques and developed using the CRISP-DM methodology.Book chapterhttps://doi.org/10.4018/978-1-7998-2351-3.ch009W4229816163enIGI Global eBooks2019-09-26 |
| spellingShingle | neonatal morbidity maternal morbidity predictive analytics Bayesian networks influence diagrams risk factors newborn health medical records data mining forecasting models Artificial Intelligence in Healthcare Maternal and Neonatal Healthcare Data Mining and Machine Learning Applications Edgardo Palza V Jorge Sanchez Guillermo Mamani Percy Pacora Alain Abran Jane Dominique Moon A Predictive Analytic Model for Maternal Morbidity |
| title | A Predictive Analytic Model for Maternal Morbidity |
| title_full | A Predictive Analytic Model for Maternal Morbidity |
| title_fullStr | A Predictive Analytic Model for Maternal Morbidity |
| title_full_unstemmed | A Predictive Analytic Model for Maternal Morbidity |
| title_short | A Predictive Analytic Model for Maternal Morbidity |
| title_sort | predictive analytic model for maternal morbidity |
| topic | neonatal morbidity maternal morbidity predictive analytics Bayesian networks influence diagrams risk factors newborn health medical records data mining forecasting models Artificial Intelligence in Healthcare Maternal and Neonatal Healthcare Data Mining and Machine Learning Applications |
| topic_facet | neonatal morbidity maternal morbidity predictive analytics Bayesian networks influence diagrams risk factors newborn health medical records data mining forecasting models Artificial Intelligence in Healthcare Maternal and Neonatal Healthcare Data Mining and Machine Learning Applications |
| url | https://doi.org/10.4018/978-1-7998-2351-3.ch009 |
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