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Agrupamiento de fenotipos establecidos a partir de aprendizaje de máquina no supervisado para pacientes con Sepsis Neonatal No Confirmada y menor a 33 semanas según tasa de uso de antibióticos

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dc.contributor.advisorOrjuela Cañón, Alvaro David
dc.creatorBeltrán Gasca, Juan Carlos
dc.creator.degreeMagíster en Ingeniería Biomédica
dc.creator.degreeLevelMaestría
dc.date.accessioned2026-02-05T21:13:54Z
dc.date.available2026-02-05T21:13:54Z
dc.date.created2025-09-19
dc.descriptionLa sepsis neonatal causa mucha mortalidad y morbilidad sobre todo en la población de prematuros, aclarando que no hay consenso en su definición y que la sospecha se basa fundamentalmente en el cambio de la evolución del neonato examinado, lo que condiciona al médico tratante al inicio de antibióticos de manera empírica con amplia cobertura y con una duración muy variada. Estos dos últimos factores conllevan a mayor morbi- mortalidad. Insistir en esta asociación es muy importante por que como se demuestra en estudios recientes, aunque se ha logrado con el tiempo una disminución en los días de tratamiento, aún no se ha logrado disminuir el número de neonatos expuestos a los antibióticos empíricos. Se pretende utilizar una estrategia de aprendizaje de máquina para la búsqueda de esta asociación entre duración de antibióticos empíricos y muerte en una base de datos anónimos de libre acceso en la que todos los pacientes fueron evaluados y tratados por sospecha de sepsis temprana o sepsis tardía.
dc.description.abstractNeonatal sepsis causes significant mortality and morbidity, particularly in premature infants. It's important to clarify that there is no consensus on its definition, and suspicion is primarily based on changes in the neonate's clinical course. This often leads the attending physician to initiate empirical antibiotics with broad coverage and varying durations. These last two factors contribute to increased morbidity and mortality. Emphasizing this association is crucial because, as recent studies have shown, although the duration of treatment has decreased over time, the number of neonates exposed to empirical antibiotics has not yet been reduced. This study aims to use a machine learning strategy to investigate this association between the duration of empirical antibiotics and death in an open-access, anonymous database where all patients were evaluated and treated for suspected early-onset or late-onset sepsis.
dc.format.extent46 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_47465
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/47465
dc.language.isospa
dc.publisherUniversidad del Rosario
dc.publisherEscuela Colombiana de Ingeniería Julio Garavito
dc.publisher.departmentEscuela de Medicina y Ciencias de la Saludspa
dc.publisher.programMaestría en Ingeniería Biomédicaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.licenciaEL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma.spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.source.bibliographicCitation REFERENCIAS
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dc.source.bibliographicCitationB., O.’Sullivan (2023) Machine learning applications on neonatal sepsis treatment: a scoping review. En: BMC Infectious Diseases. Vol. 23; pp. 441 - 451;
dc.source.bibliographicCitationBenoni, R.; Balestri, E.; Endrias, T.; Tolera, J.; Borellini, M.; Calia, M.; Biasci, F.; Pisani, L. (2023) Exploring the use of cluster analysis to assess antibiotic stewardship in critically-ill neonates in a low resource setting. En: Antimicrob Resist Infect Control. Vol. 31;12(1):119; Disponible en: 10.1186/s13756-023-01325-w.
dc.source.bibliographicCitationTing, J.Y.; Synnes, A.; Roberts, A. (2016) Association between antibiotic use and neonatal mortality and morbidities in very low-birth-weight infants without culture-proven sepsis or necrotizing enterocolitis. En: JAMA Pediatr. Vol. 170; No. 12; pp. 1181 - 7;
dc.source.bibliographicCitationSullivan, B.A.; Grundmeier, R.W. (2025) Machine Learning Models as Early Warning Systems for Neonatal Infection. En: Clin Perinatol. Vol. Mar;52(1):167-183; Disponible en: 10.1016/j.clp.2024.10.011.
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dc.source.bibliographicCitationTing, J.Y.; Roberts, A.; Sherlock, R. (2019) Duration of initial empirical antibiotic therapy and outcomes in very low birth weight infants. En: Pediatrics. Vol. 143; No. 3;
dc.source.bibliographicCitationTing, J.Y.; Synnes, A.; Roberts, A. (2018) Association of antibiotic utilization and neuro-developmental outcomes among extremely low gestational age neonates without proven sepsis or necrotizing enterocolitis. En: Am J Perinatol. Vol. 35; No. 10; pp. 972 - 8;
dc.source.bibliographicCitationHan, J.; Kamber, M.; Pei, J. (2012) Data Mining: Concepts and Techniques. : Elsevier;
dc.source.bibliographicCitationOstapenko, S.; Schmatz, M.; L, Srinivasan (2019) Neonatal sepsis registry: Time to antibiotic dataset. En: Data in brief. Vol. 104788;
dc.source.bibliographicCitationPedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Duchesnay, É. (2011) Scikit-learn: Machine learning in Python. En: Journal of Machine Learning Research. Vol. 12; pp. 2825 - 2830;
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dc.source.bibliographicCitationGibson, S.; Vreeswijk, C.; Hennequin, G. (2023) Self-organizing maps for interpretable dimensionality reduction and clustering of high-dimensional data. En: Neural Networks. Vol. 160; pp. 155 - 168;
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dc.source.bibliographicCitationFung, G.P.; Ting, J.Y. (2025) Re-Thinking the Norms of Antibiotic Prescribing in the Neonatal Intensive Care Unit. En: Clin Perinatol. Vol. Mar;52(1):133-146; Disponible en: 10.1016/j.clp.2024.10.009.
dc.source.bibliographicCitationFeng, K.; Zhang, T.; Hua, Z. (2025) Discontinuation of empirical antibiotics in suspected neonatal early-onset sepsis: a systematic review and meta-analysis. En: Pediatr Res. Disponible en: 10.1038/s41390-025-04290-9.
dc.source.bibliographicCitationOh, W.; Veshtaj, M.; Sakhuja, A. (2025) Enriching patient populations in ICU trials: reducing heterogeneity through machine learning. En: Curr Opin Crit Care. Vol. 1;31(4):410-416;
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dc.source.bibliographicCitationFN, Al Gharaibeh; M, Huang; JL, Wynn; R, Kamaleswaran; MR, Atreya; Transcriptomic mortality signature defines high-risk neonatal sepsis endotype. En: Front Immunol. Disponible en: 10.3389/fimmu.2025.1601316.
dc.source.bibliographicCitationWang, H.; Yang, R.; Chen, N.; Li, X. (2025) Heterogeneity of Neutrophils and Immunological Function in Neonatal Sepsis: Analysis of Molecular Subtypes Based on Hypoxia-Glycolysis-Lactylation. En: Mediators Inflamm. Vol. 2025; No. 5790261; Disponible en: PMC11964727. Disponible en: 10.1155/mi/5790261.
dc.source.bibliographicCitationGerard, R.; Dewitte, A.; Gross, F.; Pradeu, T.; Lemoine, M.; Goret, J.; Mamani-Matsuda, M. (2025) Is "pre-sepsis" the new sepsis? A narrative review. En: PLoS Pathog. Vol. 31;21(7):e1013372;
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dc.source.bibliographicCitationDepani, S.J.; Ladhani, S.; Heath, P.T.; Lamagni, T.L.; Johnson, A.P.; Pebody, R.G. (2011) The contribution of infections to neonatal deaths in England and Wales. En: Pediatr Infect Dis J. Vol. 30; No. 4; pp. 345 - 7;
dc.source.bibliographicCitationHayes, R. (2023) Neonatal sepsis definitions from randomised clinical trials. En: Pediatr. Res. Vol. 93; pp. 1141 - 1148;
dc.source.bibliographicCitationClark, R.H.; Bloom, B.T.; Spitzer, A.R.; Gerstmann, D.R. (2006) Empiric use of ampicillin and cefotaxime, compared with ampicillin and gentamicin, for neonates at risk for sepsis is associated with an increased risk of neonatal death. En: Pediatrics. Vol. 117; No. 1; pp. 67 - 74;
dc.source.bibliographicCitationTripathi, N.; Cotton, C.M.; Smith, P. (2012) Antibiotic use and misuse in the neonatal intensive care unit. En: Clin Perinatol. Vol. 39; No. 1; pp. 61 - 8;
dc.source.bibliographicCitationRussell, N.; Barday, M.; Dramowski, A.; Sharland, M.; Bekker, A. (2023) Early-versus Late-Onset Sepsis in Neonates—Time to Shift the Paradigm?. En: Clin Microbiol Infect. Vol. 30; pp. 38 - 43;
dc.source.bibliographicCitationRussell, A.B.; Sharland, M.; Heath, P.T. (2012) Improving antibiotic prescribing in neonatal units: time to act. En: Arch Dis Fetal Neonatal. Vol. 97:F141–6;
dc.source.bibliographicCitationCotten, C.M.; Taylor, S.; Stoll, B.; Goldberg, R.N.; Hansen, N.I.; Sánchez, P.J. (2009) Prolonged duration of initial empirical antibiotic treatment is associated with increased rates of necrotizing enterocolitis and death for extremely low birth weight infants. En: Pediatrics. Vol. 123; pp. 58 - 66;
dc.source.bibliographicCitationBlackburn, R.M.; Verlander, N.; Heath, P.; Muller-P, B. (2014) The changing antibiotic susceptibility of bloodstream infections in the first month of life: informing antibiotic policies for early- and late-onset neonatal sepsis. En: Epidemiol Infect. Vol. 142; No. 4; pp. 803 - 11;
dc.source.bibliographicCitationGeneva, World Health Organization (2019) Ten threats to global health in. Disponible en: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019.
dc.source.bibliographicCitation (2015) World Health Assembly 68. Sixty-eighth World Health Assembly. Geneva Disponible en: http://apps.who.int/gb/ebwha/pdf_files/WHA68/A68_R7-en.pdf).
dc.source.bibliographicCitationFlannery, D.D.; Zevallos Barboza, A.; Mukhopadhyay, S.; Gerber, J.S.; McDonough, M.; Shu, D.; Hennessy, S.; Wade, K.C.; Puopolo, K.M. (2024) Antibiotic use among extremely low birth-weight infants from 2009 to 2021: a retrospective observational study. En: Arch Dis Child Fetal Neonatal Ed.
dc.source.bibliographicCitationB., O.’Sullivan (2023) Machine learning applications on neonatal sepsis treatment: a scoping review. En: BMC Infectious Diseases. Vol. 23; pp. 441 - 451;
dc.source.bibliographicCitationBenoni, R.; Balestri, E.; Endrias, T.; Tolera, J.; Borellini, M.; Calia, M.; Biasci, F.; Pisani, L. (2023) Exploring the use of cluster analysis to assess antibiotic stewardship in critically-ill neonates in a low resource setting. En: Antimicrob Resist Infect Control. Vol. 31;12(1):119; Disponible en: 10.1186/s13756-023-01325-w.
dc.source.bibliographicCitationTing, J.Y.; Synnes, A.; Roberts, A. (2016) Association between antibiotic use and neonatal mortality and morbidities in very low-birth-weight infants without culture-proven sepsis or necrotizing enterocolitis. En: JAMA Pediatr. Vol. 170; No. 12; pp. 1181 - 7;
dc.source.bibliographicCitationSullivan, B.A.; Grundmeier, R.W. (2025) Machine Learning Models as Early Warning Systems for Neonatal Infection. En: Clin Perinatol. Vol. Mar;52(1):167-183; Disponible en: 10.1016/j.clp.2024.10.011.
dc.source.bibliographicCitationLawrence, S.M.; Wynn, J.L.; Kimberlin, D.W.; Cantey, J.B. (2025) Investigating antibiotics in the NICU and patient safety. En: Front Cell Infect Microbiol. Vol. 15; No. 1563940; Disponible en: 10.3389/fcimb.2025.1563940.
dc.source.bibliographicCitationTing, J.Y.; Roberts, A.; Sherlock, R. (2019) Duration of initial empirical antibiotic therapy and outcomes in very low birth weight infants. En: Pediatrics. Vol. 143; No. 3;
dc.source.bibliographicCitationTing, J.Y.; Synnes, A.; Roberts, A. (2018) Association of antibiotic utilization and neuro-developmental outcomes among extremely low gestational age neonates without proven sepsis or necrotizing enterocolitis. En: Am J Perinatol. Vol. 35; No. 10; pp. 972 - 8;
dc.source.bibliographicCitationHan, J.; Kamber, M.; Pei, J. (2012) Data Mining: Concepts and Techniques. : Elsevier;
dc.source.bibliographicCitationOstapenko, S.; Schmatz, M.; L, Srinivasan (2019) Neonatal sepsis registry: Time to antibiotic dataset. En: Data in brief. Vol. 104788;
dc.source.bibliographicCitationPedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Duchesnay, É. (2011) Scikit-learn: Machine learning in Python. En: Journal of Machine Learning Research. Vol. 12; pp. 2825 - 2830;
dc.source.bibliographicCitationJain, A.K. (2010) Data clustering: 50 years beyond K-means. En: Pattern Recognition Letters. Vol. 31; No. 8; pp. 651 - 666; Disponible en: https://doi.org/10.1016/j.patrec.2009.09.011. Disponible en: 10.1016/j.patrec.2009.09.011.
dc.source.bibliographicCitationEster, M.; Kriegel, H.P.; Sander, J.; Xu, X. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. pp. 226 - 231;
dc.source.bibliographicCitationJain, A.K.; Murty, M.N.; Flynn, P.J. (1999) Data clustering: A review. En: ACM Computing Surveys (CSUR. Vol. 31; No. 3; pp. 264 - 323; Disponible en: https://doi.org/10.1145/331499.331504. Disponible en: 10.1145/331499.331504.
dc.source.bibliographicCitationGibson, S.; Vreeswijk, C.; Hennequin, G. (2023) Self-organizing maps for interpretable dimensionality reduction and clustering of high-dimensional data. En: Neural Networks. Vol. 160; pp. 155 - 168;
dc.source.bibliographicCitationVinué, L.; Pons, J.; Martí, R. (2023) A comprehensive review of internal clustering validation measures: Addressing issues in Silhouette, Calinski–Harabasz, Davies–Bouldin indices. En: Pattern Recognition Letters. Vol. 171; pp. 83 - 94;
dc.source.bibliographicCitationKoutroulis, I.; Velez, T.; Wang, T.; Yohannes, S.; Galarraga, J.E.; Morales, J.A. (2022) Pediatric sepsis phenotypes for enhanced therapeutics: an application of clustering to electronic health records. En: J Am Coll Emerg Physicians Open. Vol. 3; No. 1; Disponible en: 10.1002/emp2.12660.
dc.source.bibliographicCitationGuérin, A.; Chauvet, P.; Saubion, F. (2024) A Survey on Recent Advances in Self‐Organizing Maps. Disponible en: https://arxiv.org/abs/2501.08416.
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dc.source.bibliographicCitationFeng, K.; Zhang, T.; Hua, Z. (2025) Discontinuation of empirical antibiotics in suspected neonatal early-onset sepsis: a systematic review and meta-analysis. En: Pediatr Res. Disponible en: 10.1038/s41390-025-04290-9.
dc.source.bibliographicCitationOh, W.; Veshtaj, M.; Sakhuja, A. (2025) Enriching patient populations in ICU trials: reducing heterogeneity through machine learning. En: Curr Opin Crit Care. Vol. 1;31(4):410-416;
dc.source.bibliographicCitationFeuerriegel, S.; Frauen, D.; Melnychuk, V.; Schweisthal, J.; Hess, K.; Curth, A.; Bauer, S.; Kilbertus, N.; Kohane, I.S.; Schaar, M. (2024) Causal machine learning for predicting treatment outcomes. En: Nat Med. Vol. Apr;30(4):958-968; Disponible en: 10.1038/s41591-024-02902-1.
dc.source.bibliographicCitationDeMerle, K.M.; Angus, D.C.; Baillie, J.K. (2021) Sepsis subclasses: a framework for development and interpretation. En: Crit Care Med. Vol. 49; pp. 748 - 59;
dc.source.bibliographicCitationFN, Al Gharaibeh; M, Huang; JL, Wynn; R, Kamaleswaran; MR, Atreya; Transcriptomic mortality signature defines high-risk neonatal sepsis endotype. En: Front Immunol. Disponible en: 10.3389/fimmu.2025.1601316.
dc.source.bibliographicCitationWang, H.; Yang, R.; Chen, N.; Li, X. (2025) Heterogeneity of Neutrophils and Immunological Function in Neonatal Sepsis: Analysis of Molecular Subtypes Based on Hypoxia-Glycolysis-Lactylation. En: Mediators Inflamm. Vol. 2025; No. 5790261; Disponible en: PMC11964727. Disponible en: 10.1155/mi/5790261.
dc.source.bibliographicCitationGerard, R.; Dewitte, A.; Gross, F.; Pradeu, T.; Lemoine, M.; Goret, J.; Mamani-Matsuda, M. (2025) Is "pre-sepsis" the new sepsis? A narrative review. En: PLoS Pathog. Vol. 31;21(7):e1013372;
dc.source.bibliographicCitation REFERENCIAS
dc.source.bibliographicCitationDepani, S.J.; Ladhani, S.; Heath, P.T.; Lamagni, T.L.; Johnson, A.P.; Pebody, R.G. (2011) The contribution of infections to neonatal deaths in England and Wales. En: Pediatr Infect Dis J. Vol. 30; No. 4; pp. 345 - 7;
dc.source.bibliographicCitationHayes, R. (2023) Neonatal sepsis definitions from randomised clinical trials. En: Pediatr. Res. Vol. 93; pp. 1141 - 1148;
dc.source.bibliographicCitationClark, R.H.; Bloom, B.T.; Spitzer, A.R.; Gerstmann, D.R. (2006) Empiric use of ampicillin and cefotaxime, compared with ampicillin and gentamicin, for neonates at risk for sepsis is associated with an increased risk of neonatal death. En: Pediatrics. Vol. 117; No. 1; pp. 67 - 74;
dc.source.bibliographicCitationTripathi, N.; Cotton, C.M.; Smith, P. (2012) Antibiotic use and misuse in the neonatal intensive care unit. En: Clin Perinatol. Vol. 39; No. 1; pp. 61 - 8;
dc.source.bibliographicCitationRussell, N.; Barday, M.; Dramowski, A.; Sharland, M.; Bekker, A. (2023) Early-versus Late-Onset Sepsis in Neonates—Time to Shift the Paradigm?. En: Clin Microbiol Infect. Vol. 30; pp. 38 - 43;
dc.source.bibliographicCitationRussell, A.B.; Sharland, M.; Heath, P.T. (2012) Improving antibiotic prescribing in neonatal units: time to act. En: Arch Dis Fetal Neonatal. Vol. 97:F141–6;
dc.source.bibliographicCitationCotten, C.M.; Taylor, S.; Stoll, B.; Goldberg, R.N.; Hansen, N.I.; Sánchez, P.J. (2009) Prolonged duration of initial empirical antibiotic treatment is associated with increased rates of necrotizing enterocolitis and death for extremely low birth weight infants. En: Pediatrics. Vol. 123; pp. 58 - 66;
dc.source.bibliographicCitationBlackburn, R.M.; Verlander, N.; Heath, P.; Muller-P, B. (2014) The changing antibiotic susceptibility of bloodstream infections in the first month of life: informing antibiotic policies for early- and late-onset neonatal sepsis. En: Epidemiol Infect. Vol. 142; No. 4; pp. 803 - 11;
dc.source.bibliographicCitationGeneva, World Health Organization (2019) Ten threats to global health in. Disponible en: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019.
dc.source.bibliographicCitation (2015) World Health Assembly 68. Sixty-eighth World Health Assembly. Geneva Disponible en: http://apps.who.int/gb/ebwha/pdf_files/WHA68/A68_R7-en.pdf).
dc.source.bibliographicCitationFlannery, D.D.; Zevallos Barboza, A.; Mukhopadhyay, S.; Gerber, J.S.; McDonough, M.; Shu, D.; Hennessy, S.; Wade, K.C.; Puopolo, K.M. (2024) Antibiotic use among extremely low birth-weight infants from 2009 to 2021: a retrospective observational study. En: Arch Dis Child Fetal Neonatal Ed.
dc.source.bibliographicCitationB., O.’Sullivan (2023) Machine learning applications on neonatal sepsis treatment: a scoping review. En: BMC Infectious Diseases. Vol. 23; pp. 441 - 451;
dc.source.bibliographicCitationBenoni, R.; Balestri, E.; Endrias, T.; Tolera, J.; Borellini, M.; Calia, M.; Biasci, F.; Pisani, L. (2023) Exploring the use of cluster analysis to assess antibiotic stewardship in critically-ill neonates in a low resource setting. En: Antimicrob Resist Infect Control. Vol. 31;12(1):119; Disponible en: 10.1186/s13756-023-01325-w.
dc.source.bibliographicCitationTing, J.Y.; Synnes, A.; Roberts, A. (2016) Association between antibiotic use and neonatal mortality and morbidities in very low-birth-weight infants without culture-proven sepsis or necrotizing enterocolitis. En: JAMA Pediatr. Vol. 170; No. 12; pp. 1181 - 7;
dc.source.bibliographicCitationSullivan, B.A.; Grundmeier, R.W. (2025) Machine Learning Models as Early Warning Systems for Neonatal Infection. En: Clin Perinatol. Vol. Mar;52(1):167-183; Disponible en: 10.1016/j.clp.2024.10.011.
dc.source.bibliographicCitationLawrence, S.M.; Wynn, J.L.; Kimberlin, D.W.; Cantey, J.B. (2025) Investigating antibiotics in the NICU and patient safety. En: Front Cell Infect Microbiol. Vol. 15; No. 1563940; Disponible en: 10.3389/fcimb.2025.1563940.
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dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectSepsis neonatal
dc.subjectTasa de uso de antibióticos (AUR)
dc.subjectAprendizaje de máquina
dc.subjectMortalidad
dc.subject.keywordNeonatal Sepsis
dc.subject.keywordAntibiotic Use Rate (AUR)
dc.subject.keywordMachine Learning
dc.subject.keywordMortality
dc.titleAgrupamiento de fenotipos establecidos a partir de aprendizaje de máquina no supervisado para pacientes con Sepsis Neonatal No Confirmada y menor a 33 semanas según tasa de uso de antibióticos
dc.title.TranslatedTitleGrouping of Phenotypes established from Unsupervised Machine Learning for patients with Unconfirmed Neonatal Sepsis and less than 33 weeks according to Antibiotic Use Rate
dc.typemasterThesis
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTesis de maestría
local.department.reportEscuela de Ciencias e Ingeniería
local.regionesBogotá
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