A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study
Perinatal mortality is the death that happens between 22 weeks of gestation and the first seven days of birth. This has become an essential indicator for measuring the quality of maternal and childcare in Low-and-Middle-Income Countries (LMICs). Tools based on Artificial Intelligence (AI) have emerg...
- Autores:
-
Arias Fonseca, Sebastian
Ortiz Barrios, Miguel Angel
Konios, Alexandros
Gutierrez de Piñeres Jalile, Martha
Montero Estrada, María
Hernández Lalinde, Carlos
Medina Pacheco, Eliecer
Lambraño Coronado, Fanny
Figueroa Salazar, Ibett
Araujo Torres, Jesús
Prasca de la Hoz, Richard
- Tipo de recurso:
- Conferencia (Ponencia)
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/14090
- Acceso en línea:
- https://hdl.handle.net/11323/14090
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial Intelligence (AI)
Healthcare
Low-and-Middle-Income Countries (LMICs)
Perinatal Mortality
Random Forest (RF)
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study |
| title |
A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study |
| spellingShingle |
A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study Artificial Intelligence (AI) Healthcare Low-and-Middle-Income Countries (LMICs) Perinatal Mortality Random Forest (RF) |
| title_short |
A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study |
| title_full |
A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study |
| title_fullStr |
A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study |
| title_full_unstemmed |
A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study |
| title_sort |
A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case study |
| dc.creator.fl_str_mv |
Arias Fonseca, Sebastian Ortiz Barrios, Miguel Angel Konios, Alexandros Gutierrez de Piñeres Jalile, Martha Montero Estrada, María Hernández Lalinde, Carlos Medina Pacheco, Eliecer Lambraño Coronado, Fanny Figueroa Salazar, Ibett Araujo Torres, Jesús Prasca de la Hoz, Richard |
| dc.contributor.author.none.fl_str_mv |
Arias Fonseca, Sebastian Ortiz Barrios, Miguel Angel Konios, Alexandros Gutierrez de Piñeres Jalile, Martha Montero Estrada, María Hernández Lalinde, Carlos Medina Pacheco, Eliecer Lambraño Coronado, Fanny Figueroa Salazar, Ibett Araujo Torres, Jesús Prasca de la Hoz, Richard |
| dc.subject.proposal.eng.fl_str_mv |
Artificial Intelligence (AI) Healthcare Low-and-Middle-Income Countries (LMICs) Perinatal Mortality Random Forest (RF) |
| topic |
Artificial Intelligence (AI) Healthcare Low-and-Middle-Income Countries (LMICs) Perinatal Mortality Random Forest (RF) |
| description |
Perinatal mortality is the death that happens between 22 weeks of gestation and the first seven days of birth. This has become an essential indicator for measuring the quality of maternal and childcare in Low-and-Middle-Income Countries (LMICs). Tools based on Artificial Intelligence (AI) have emerged with immediate relevance in medical contexts, more precisely with Machine Learning (ML) tools due to the ability to learn from past and present observations and be able to generate future predictions, promising positive results in maternal and childcare processes. This paper presents a Random Forest (RF) model for predicting the risk of perinatal mortality in LMICs. We initially characterized the prenatal control process in LMICs. Second, potentially predictive features of perinatal mortality were identified considering the literature review and medical expertise. Subsequently, a data pre-processing procedure was executed to improve the data quality. The RF algorithm was employed to model the risk of perinatal mortality based on social and clinical variables. A case study in a Colombian healthcare institution was used to validate the proposed approach. The results show an RF model with an accuracy of 99.16%, sensibility = 87.50%, and specificity = 100%. |
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2024 |
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2025-04-04T16:11:13Z |
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2025-04-04T16:11:13Z |
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Arias-Fonseca, S. et al. (2024). A Machine Learning Model for Predicting the Risk of Perinatal Mortality in Low-and-Middle-Income Countries: A Case Study. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2024. Lecture Notes in Computer Science, vol 14710. Springer, Cham. https://doi.org/10.1007/978-3-031-61063-9_16 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Arias-Fonseca, S. et al. (2024). A Machine Learning Model for Predicting the Risk of Perinatal Mortality in Low-and-Middle-Income Countries: A Case Study. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2024. Lecture Notes in Computer Science, vol 14710. Springer, Cham. https://doi.org/10.1007/978-3-031-61063-9_16 10.1007/978-3-031-61063-9_16 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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Alves, L.C., Beluzo, C.E., Arruda, N.M., Bresan, R.C., Carvalho, T. (2020) Assessing the Performance of Machine Learning Models to Predict Neonatal Mortality Risk in Brazil, 2000–2016, Mortality prediction based on imbalanced new born and perinatal period data IJACSA Int. J. Adv. Comput. Sci. Appl, Bhutta, Z.A. Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? (2014) Lancet, 384 (9940), pp. 347-370. Blencowe, H. National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: A systematic analysis (2016) Lancet Glob. Health, 4 (2), pp. e98-e108. Bogale, D.S., Abuhay, T.M., Dejene, B.E. Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods (2022) BMC Med. Inform. Decis. Mak., 22 (1), p. 341. Esteva, A. A guide to deep learning in healthcare (2019) Nat. Med., 25 (1), pp. 24-29. Flenady, V. Stillbirths: The way forward in high-income countries (2011) Lancet, 377 (9778), pp. 1703-1717. García, G.A., Prada, G.E., Baracaldo, M.J., Jaimes, A.P. Perinatal mortality in a high-complexity hospital in Colombia: An analysis of causes and associated factors (2020) Revista De Salud Pública, 22 (2), pp. 1-7. Gaviria, A., Guzman, J.M., Uribe, L.F. Factors associated with perinatal mortality in women treated at a teaching hospital in Quibdó, Chocó, Colombia (2018) Biomedica: Revista Del Instituto Nacional De Salud, 38 (3), pp. 344-353. Lawn, J.E. Stillbirths: Rates, risk factors, and acceleration towards 2030 (2016) Lancet, 387 (10018), pp. 587-603. Malacova, E. Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 (2020) Sci. Rep., 10 (1), p. 5354. (2021) Ministerio De Salud Y Protección Social De Colombia: Indicadores Básicos De Salud Colombia, Mboya, I.B., Mahande, M.J., Mohammed, M., Obure, J., Mwambi, H.G. Prediction of perinatal death using machine learning models: A birth registry-based cohort study in northern Tanzania (2020) BMJ Open, 10 (10). Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T. Deep learning for healthcare: Review, opportunities and challenges (2017) Brief. Bioinform., 19 (6), pp. 1236-1246. Padula, A.M. A review of maternal prenatal exposures to environmental chemicals and psychosocial stressors—implications for research on perinatal outcomes in the ECHO program (2020) J. Perinatol., 40 (1), pp. 10-24. MMWR-racial/ethnic disparities in pregnancy-related deaths — United States, 2007– 2016 MMWR Morb. Mortal. Wkly Rep, Rajkomar, A., Dean, J., Kohane, I. Machine learning in medicine (2019) N. Engl. J. Med., 380 (14), pp. 1347-1358. Ramakrishnan, R., Rao, S., He, J.R. Perinatal health predictors using artificial intelligence: a review (2021) Women’s Health, 17. Saravanou, A., Noelke, C., Huntington, N., Acevedo-Garcia, D., Gunopulos, D. Predictive modeling of infant mortality (2021) Data Min. Knowl. Discov., 35 (4), pp. 1785-1807. Smith, L.K. Quantifying the burden of stillbirths before 28 weeks of completed gestational age in high-income countries: A population-based study of 19 European countries (2018) Lancet, 392 (10158), pp. 1639-1646. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arias Fonseca, Sebastianvirtual::1027-1Ortiz Barrios, Miguel Angelvirtual::1028-1Konios, AlexandrosGutierrez de Piñeres Jalile, MarthaMontero Estrada, MaríaHernández Lalinde, CarlosMedina Pacheco, EliecerLambraño Coronado, FannyFigueroa Salazar, IbettAraujo Torres, JesúsPrasca de la Hoz, Richard2025-04-04T16:11:13Z2025-04-04T16:11:13Z2024-06-01Arias-Fonseca, S. et al. (2024). A Machine Learning Model for Predicting the Risk of Perinatal Mortality in Low-and-Middle-Income Countries: A Case Study. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2024. Lecture Notes in Computer Science, vol 14710. Springer, Cham. https://doi.org/10.1007/978-3-031-61063-9_16https://hdl.handle.net/11323/1409010.1007/978-3-031-61063-9_16Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Perinatal mortality is the death that happens between 22 weeks of gestation and the first seven days of birth. This has become an essential indicator for measuring the quality of maternal and childcare in Low-and-Middle-Income Countries (LMICs). Tools based on Artificial Intelligence (AI) have emerged with immediate relevance in medical contexts, more precisely with Machine Learning (ML) tools due to the ability to learn from past and present observations and be able to generate future predictions, promising positive results in maternal and childcare processes. This paper presents a Random Forest (RF) model for predicting the risk of perinatal mortality in LMICs. We initially characterized the prenatal control process in LMICs. Second, potentially predictive features of perinatal mortality were identified considering the literature review and medical expertise. Subsequently, a data pre-processing procedure was executed to improve the data quality. The RF algorithm was employed to model the risk of perinatal mortality based on social and clinical variables. A case study in a Colombian healthcare institution was used to validate the proposed approach. The results show an RF model with an accuracy of 99.16%, sensibility = 87.50%, and specificity = 100%.18 páginasapplication/pdfengSpringer VerlagGermanyhttps://link.springer.com/chapter/10.1007/978-3-031-61063-9_16A machine learning model for predicting the risk of perinatal mortality in low-and-middle-income countries: A case studyDocumento de Conferenciahttp://purl.org/coar/resource_type/c_8544http://purl.org/coar/resource_type/c_8042http://purl.org/coar/resource_type/c_c94fTextinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/redcol/resource_type/WPinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a852024-06-09/2024-07-04Washington, DC, USAAlves, L.C., Beluzo, C.E., Arruda, N.M., Bresan, R.C., Carvalho, T. (2020) Assessing the Performance of Machine Learning Models to Predict Neonatal Mortality Risk in Brazil, 2000–2016,Mortality prediction based on imbalanced new born and perinatal period data IJACSA Int. J. Adv. Comput. Sci. Appl,Bhutta, Z.A. Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? (2014) Lancet, 384 (9940), pp. 347-370.Blencowe, H. National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: A systematic analysis (2016) Lancet Glob. Health, 4 (2), pp. e98-e108.Bogale, D.S., Abuhay, T.M., Dejene, B.E. Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods (2022) BMC Med. Inform. Decis. Mak., 22 (1), p. 341.Esteva, A. A guide to deep learning in healthcare (2019) Nat. Med., 25 (1), pp. 24-29.Flenady, V. Stillbirths: The way forward in high-income countries (2011) Lancet, 377 (9778), pp. 1703-1717.García, G.A., Prada, G.E., Baracaldo, M.J., Jaimes, A.P. Perinatal mortality in a high-complexity hospital in Colombia: An analysis of causes and associated factors (2020) Revista De Salud Pública, 22 (2), pp. 1-7.Gaviria, A., Guzman, J.M., Uribe, L.F. Factors associated with perinatal mortality in women treated at a teaching hospital in Quibdó, Chocó, Colombia (2018) Biomedica: Revista Del Instituto Nacional De Salud, 38 (3), pp. 344-353.Lawn, J.E. Stillbirths: Rates, risk factors, and acceleration towards 2030 (2016) Lancet, 387 (10018), pp. 587-603.Malacova, E. Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 (2020) Sci. Rep., 10 (1), p. 5354.(2021) Ministerio De Salud Y Protección Social De Colombia: Indicadores Básicos De Salud Colombia,Mboya, I.B., Mahande, M.J., Mohammed, M., Obure, J., Mwambi, H.G. Prediction of perinatal death using machine learning models: A birth registry-based cohort study in northern Tanzania (2020) BMJ Open, 10 (10).Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T. Deep learning for healthcare: Review, opportunities and challenges (2017) Brief. Bioinform., 19 (6), pp. 1236-1246.Padula, A.M. A review of maternal prenatal exposures to environmental chemicals and psychosocial stressors—implications for research on perinatal outcomes in the ECHO program (2020) J. Perinatol., 40 (1), pp. 10-24.MMWR-racial/ethnic disparities in pregnancy-related deaths — United States, 2007– 2016 MMWR Morb. Mortal. Wkly Rep,Rajkomar, A., Dean, J., Kohane, I. Machine learning in medicine (2019) N. Engl. J. Med., 380 (14), pp. 1347-1358.Ramakrishnan, R., Rao, S., He, J.R. Perinatal health predictors using artificial intelligence: a review (2021) Women’s Health, 17.Saravanou, A., Noelke, C., Huntington, N., Acevedo-Garcia, D., Gunopulos, D. Predictive modeling of infant mortality (2021) Data Min. Knowl. Discov., 35 (4), pp. 1785-1807.Smith, L.K. Quantifying the burden of stillbirths before 28 weeks of completed gestational age in high-income countries: A population-based study of 19 European countries (2018) Lancet, 392 (10158), pp. 1639-1646.Artificial Intelligence (AI)HealthcareLow-and-Middle-Income Countries (LMICs)Perinatal MortalityRandom Forest (RF)Publication1436bd93-a006-439d-9573-bea269253667virtual::1027-1b2762503-902d-495c-a50a-491512d065a4virtual::1028-11436bd93-a006-439d-9573-bea269253667virtual::1027-1b2762503-902d-495c-a50a-491512d065a4virtual::1028-10000-0002-5067-2839virtual::1027-10000-0001-6890-7547virtual::1028-1ORIGINALA Machine Learning Model for Predicting the Risk of Perinatal Mortality in Low-and-Middle-Income Countries A Case Study.pdfA Machine Learning Model for Predicting the Risk of Perinatal Mortality in Low-and-Middle-Income Countries A Case 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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