Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática

Introducción: La evaluación del riesgo de sesgo de ensayos clínicos es una actividad crítica en el desarrollo de revisiones sistemáticas. El aprendizaje de máquinas podría disminuir la variabilidad y subjetividad inherente a este proceso. Objetivo: Determinar la validez y concordancia del aprendizaj...

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Autores:
Bautista Mier, Heider Alexis
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/17464
Acceso en línea:
http://hdl.handle.net/20.500.12749/17464
Palabra clave:
Medical sciences
Health sciences
Clinical trials
Validity and consistency
Systematic review
Databases
Bibliographic searches
Machine learning
Artificial intelligence
Ciencias médicas
Bases de datos
Búsquedas bibliográficas
Aprendizaje automático
Inteligencia artificial
Ciencias de la salud
Ensayos clínicos
Validez y concordancia
Revisión sistemática
Rights
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
id UNAB2_298776c91be568bb7e853d90c2a2b605
oai_identifier_str oai:repository.unab.edu.co:20.500.12749/17464
network_acronym_str UNAB2
network_name_str Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
dc.title.translated.spa.fl_str_mv Validity and consistency of machine learning in the assessment of risk of bias in randomized clinical trials. Systematic review
title Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
spellingShingle Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
Medical sciences
Health sciences
Clinical trials
Validity and consistency
Systematic review
Databases
Bibliographic searches
Machine learning
Artificial intelligence
Ciencias médicas
Bases de datos
Búsquedas bibliográficas
Aprendizaje automático
Inteligencia artificial
Ciencias de la salud
Ensayos clínicos
Validez y concordancia
Revisión sistemática
title_short Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
title_full Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
title_fullStr Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
title_full_unstemmed Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
title_sort Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemática
dc.creator.fl_str_mv Bautista Mier, Heider Alexis
dc.contributor.advisor.none.fl_str_mv Vásquez Hernández, Skarlet Marcell
Barreto Montenegro, Alexis Eduardo
Moreno, Karen Julieth
dc.contributor.author.none.fl_str_mv Bautista Mier, Heider Alexis
dc.contributor.cvlac.spa.fl_str_mv Vásquez Hernández, Skarlet Marcell [0001349039]
Barreto Montenegro, Alexis Eduardo [0000108751]
dc.contributor.googlescholar.spa.fl_str_mv Vásquez Hernández, Skarlet Marcell [IyVfYugAAAAJ]
Barreto Montenegro, Alexis Eduardo [oIh_fLEAAAAJ&hl]
dc.contributor.orcid.spa.fl_str_mv Vásquez Hernández, Skarlet Marcell [0000-0003-2552-9819]
Barreto Montenegro, Alexis Eduardo [0000-0001-7905-7023]
dc.contributor.scopus.spa.fl_str_mv Vásquez Hernández, Skarlet Marcell [56057064900]
dc.contributor.researchgate.spa.fl_str_mv Vásquez Hernández, Skarlet Marcell [Skarlet_Vasquez]
Barreto Montenegro, Alexis Eduardo [Alexis-Eduardo-Barreto-Montenegro-2183697377]
dc.contributor.linkedin.none.fl_str_mv Barreto Montenegro, Alexis Eduardo [alexis-eduardo-barreto-montenegro-704a7a27]
dc.subject.keywords.spa.fl_str_mv Medical sciences
Health sciences
Clinical trials
Validity and consistency
Systematic review
Databases
Bibliographic searches
Machine learning
Artificial intelligence
topic Medical sciences
Health sciences
Clinical trials
Validity and consistency
Systematic review
Databases
Bibliographic searches
Machine learning
Artificial intelligence
Ciencias médicas
Bases de datos
Búsquedas bibliográficas
Aprendizaje automático
Inteligencia artificial
Ciencias de la salud
Ensayos clínicos
Validez y concordancia
Revisión sistemática
dc.subject.lemb.spa.fl_str_mv Ciencias médicas
Bases de datos
Búsquedas bibliográficas
Aprendizaje automático
Inteligencia artificial
dc.subject.proposal.spa.fl_str_mv Ciencias de la salud
Ensayos clínicos
Validez y concordancia
Revisión sistemática
description Introducción: La evaluación del riesgo de sesgo de ensayos clínicos es una actividad crítica en el desarrollo de revisiones sistemáticas. El aprendizaje de máquinas podría disminuir la variabilidad y subjetividad inherente a este proceso. Objetivo: Determinar la validez y concordancia del aprendizaje de máquinas en la automatización de la evaluación de riesgos de sesgos de estudios clínicos primarios incluidos en revisión sistemática. Metodología: Se realizó una revisión sistemática que incluyó estudios que evaluaron la validez y/o concordancia de la evaluación del riesgo de sesgos de ensayos clínicos a través de herramientas de aprendizaje de máquinas en comparación con la evaluación convencional realizada por humanos. La búsqueda fue realizada en Medline, SCOPUS, LILACS, Springer Link, scienceDirect y Google scholar. La selección y evaluación del riesgo de sesgo de los estudios fue realizado por dos investigadores de manera independiente, los desacuerdos fueron resueltos mediante consenso. Los resultados se presentaron mediante una síntesis cualitativa. La síntesis cuantitativa de la información se realizó como ejercicio académico en el software Metadisc 2, se estimaron sensibilidad y especificidad como medidas resumen.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-08-24T18:58:19Z
dc.date.available.none.fl_str_mv 2022-08-24T18:58:19Z
dc.date.issued.none.fl_str_mv 2022
dc.type.eng.fl_str_mv Thesis
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.local.spa.fl_str_mv Tesis
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12749/17464
dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UNAB
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.unab.edu.co
url http://hdl.handle.net/20.500.12749/17464
identifier_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
reponame:Repositorio Institucional UNAB
repourl:https://repository.unab.edu.co
dc.language.iso.spa.fl_str_mv spa
language spa
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spelling Vásquez Hernández, Skarlet Marcell8b710a40-acd2-4858-9c46-b5c7dc2c38ddBarreto Montenegro, Alexis Eduardo4291d8b5-35ab-4f36-bbe8-24e4f94e9ca0Moreno, Karen Julieth1b66d0ad-0558-4dd4-bb8a-c6e84344e862Bautista Mier, Heider Alexis74a18645-5941-498f-8a93-2f0295125b9bVásquez Hernández, Skarlet Marcell [0001349039]Barreto Montenegro, Alexis Eduardo [0000108751]Vásquez Hernández, Skarlet Marcell [IyVfYugAAAAJ]Barreto Montenegro, Alexis Eduardo [oIh_fLEAAAAJ&hl]Vásquez Hernández, Skarlet Marcell [0000-0003-2552-9819]Barreto Montenegro, Alexis Eduardo [0000-0001-7905-7023]Vásquez Hernández, Skarlet Marcell [56057064900]Vásquez Hernández, Skarlet Marcell [Skarlet_Vasquez]Barreto Montenegro, Alexis Eduardo [Alexis-Eduardo-Barreto-Montenegro-2183697377]Barreto Montenegro, Alexis Eduardo [alexis-eduardo-barreto-montenegro-704a7a27]ColombiaUNAB Campus Bucaramanga2022-08-24T18:58:19Z2022-08-24T18:58:19Z2022http://hdl.handle.net/20.500.12749/17464instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coIntroducción: La evaluación del riesgo de sesgo de ensayos clínicos es una actividad crítica en el desarrollo de revisiones sistemáticas. El aprendizaje de máquinas podría disminuir la variabilidad y subjetividad inherente a este proceso. Objetivo: Determinar la validez y concordancia del aprendizaje de máquinas en la automatización de la evaluación de riesgos de sesgos de estudios clínicos primarios incluidos en revisión sistemática. Metodología: Se realizó una revisión sistemática que incluyó estudios que evaluaron la validez y/o concordancia de la evaluación del riesgo de sesgos de ensayos clínicos a través de herramientas de aprendizaje de máquinas en comparación con la evaluación convencional realizada por humanos. La búsqueda fue realizada en Medline, SCOPUS, LILACS, Springer Link, scienceDirect y Google scholar. La selección y evaluación del riesgo de sesgo de los estudios fue realizado por dos investigadores de manera independiente, los desacuerdos fueron resueltos mediante consenso. Los resultados se presentaron mediante una síntesis cualitativa. La síntesis cuantitativa de la información se realizó como ejercicio académico en el software Metadisc 2, se estimaron sensibilidad y especificidad como medidas resumen.1. TÍTULO DEL PROYECTO 5 2. RESUMEN DEL PROYECTO 5 3. DESCRIPCIÓN DEL PROYECTO 7 4. MARCO TEÓRICO 10 5. ESTADO DEL ARTE 13 6. OBJETIVOS 13 7. METODOLOGÍA 15 8. RESULTADOS 18 9. DISCUSIÓN 35 10. CONCLUSIONES 37 11. REFERENCIAS 38MaestríaIntroduction: The evaluation of the risk of bias in clinical trials is a critical activity in the development of systematic reviews. Machine learning could reduce the variability and subjectivity inherent in this process. Objective: To determine the validity and consistency of machine learning in the automation of bias risk assessment of primary clinical studies included in a systematic review. Methodology: A systematic review was carried out that included studies that evaluated the validity and/or concordance of the assessment of the risk of bias of clinical trials through machine learning tools in comparison with the conventional evaluation carried out by humans. The search was carried out in Medline, SCOPUS, LILACS, Springer Link, scienceDirect and Google scholar. The selection and evaluation of the risk of bias of the studies was carried out by two researchers independently, disagreements were resolved by consensus. The results were presented through a qualitative synthesis. The quantitative synthesis of the information was carried out as an academic exercise in the Metadisc 2 software, sensitivity and specificity were estimated as summary measures.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Validez y concordancia del aprendizaje de máquinas en la evaluación de riesgo de sesgos de ensayos clínicos aleatorizados. Revisión sistemáticaValidity and consistency of machine learning in the assessment of risk of bias in randomized clinical trials. Systematic reviewThesisinfo:eu-repo/semantics/masterThesisTesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TMMagíster en Métodos para la Producción y Aplicación de Conocimiento Científico en SaludUniversidad Autónoma de Bucaramanga UNABFacultad Ciencias de la SaludMaestría en Métodos para la Producción y Aplicación de Conocimiento Científico en SaludMedical sciencesHealth sciencesClinical trialsValidity and consistencySystematic reviewDatabasesBibliographic searchesMachine learningArtificial intelligenceCiencias médicasBases de datosBúsquedas bibliográficasAprendizaje automáticoInteligencia artificialCiencias de la saludEnsayos clínicosValidez y concordanciaRevisión sistemática1. NIH. National Library of Medicine. [Internet]. Pubmed.gov. [citado 15 de abril de 2022]. Disponible en: https://pubmed.ncbi.nlm.nih.gov/2. Chalmers I. Addressing uncertainties about the effects of treatments offered to NHS patients: whose responsibility? 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Paynter R, Bañez LL, Erinoff E, Lege-Matsuura J, Potter S. Commentary on EPC methods: an exploration of the use of text-mining software in systematic reviews. J Clin Epidemiol. abril de 2017;84:33-6.24. Rathbone J, Hoffmann T, Glasziou P. Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers. Syst Rev. diciembre de 2015;4(1):8025. On behalf of the founding members of the ICASR group, Beller E, Clark J, Tsafnat G, Adams C, Diehl H, et al. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Syst Rev. diciembre de 2018;7(1):77.26. Tsou AY, Treadwell JR, Erinoff E, Schoelles K. Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer. Syst Rev. diciembre de 2020;9(1):73.27. Popoff E, Besada M, Jansen JP, Cope S, Kanters S. 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Machine learning to assist risk-of-bias assessments in systematic reviews. Int J Epidemiol. febrero de 2016;45(1):266-77.33. Higgins JPT, Altman DG, Gotzsche PC, Juni P, Moher D, Oxman AD, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 18 de octubre de 2011;343(oct18 2):d5928-d592834. Savović J, Weeks L, Sterne JA, Turner L, Altman DG, Moher D, et al. Evaluation of the Cochrane Collaboration’s tool for assessing the risk of bias in randomized trials: focus groups, online survey, proposed recommendations and their implementation. Syst Rev. diciembre de 2014;3(1):37.35. Puljak L, Ramic I, Arriola Naharro C, Brezova J, Lin YC, Surdila AA, et al. Cochrane risk of bias tool was used inadequately in the majority of non-Cochrane systematic reviews. J Clin Epidemiol. julio de 2020;123:114-936. Kotz D, West R. Key concepts in clinical epidemiology: addressing and reporting sources of bias in randomized controlled trials. J Clin Epidemiol. septiembre de 2021;S089543562100315237. Reveiz L, Chapman E, Asial S, Munoz S, Bonfill X, Alonso-Coello P. Risk of bias of randomized trials over time. J Clin Epidemiol. septiembre de 2015;68(9):1036-4538. Hartling L, Hamm MP, Milne A, Vandermeer B, Santaguida PL, Ansari M, et al. Testing the Risk of Bias tool showed low reliability between individual reviewers and across consensus assessments of reviewer pairs. J Clin Epidemiol. septiembre de 2013;66(9):973-8139. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 28 de agosto de 2019;l4898.40. Higgins J, Savović J, Page M, Elbers R, Sterne J. Chapter 8: Assessing risk of bias in a randomized trial. En: Cochrane Handbook for Systematic Reviews of Interventions version 63 [Internet]. 2022. Disponible en: www.training.cochrane.org/handbook41. Zabor EC, Kaizer AM, Hobbs BP. Randomized Controlled Trials. 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Kidney Int. enero de 2008;73(2):148-5347. Tripepi G, Jager KJ, Dekker FW, Zoccali C. Selection Bias and Information Bias in Clinical Research. Nephron Clin Pract. 2010;115(2):c94-9.48. Rivas-Ruiz F, Pérez-Vicente S, González-Ramírez AR. Bias in clinical epidemiological study designs. Allergol Immunopathol (Madr). enero de 2013;41(1):54-9.49. Savović J, Turner RM, Mawdsley D, Jones HE, Beynon R, Higgins JPT, et al. Association Between Risk-of-Bias Assessments and Results of Randomized Trials in Cochrane Reviews: The ROBES Meta-Epidemiologic Study. Am J Epidemiol. 1 de mayo de 2018;187(5):1113-22.50. Tovey D. Making sense of a systematic review. InnovAiT Educ Inspir Gen Pract. enero de 2021;14(1):45-51.51. Delgado-Rodríguez M, Sillero-Arenas M. Systematic review and meta-analysis. Med Intensiva. octubre de 2018;42(7):444-53.52. Pollock A, Berge E. How to do a systematic review. Int J Stroke. febrero de 2018;13(2):138-56.53. Chandler J, Cumpston M, Thomas J, Higgins J, Deeks J, Clarke M. 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McGrath M, Chen C, Braga LH, Farrokhyar F. Quality of reporting for pilot randomized controlled trials in the pediatric urology literature–A systematic review. Journal of Pediatric Urology. 2021 Sep;72. Soboczenski F, Trikalinos TA, Kuiper J, Bias RG, Wallace BC, Marshall IJ. Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study. BMC Med Inform Decis Mak. diciembre de 2019;19(1):96.73. Goldkuhle M, Dimaki M, Gartlehner G, Monsef I, Dahm P, Glossmann JP, et al. Nivolumab for adults with Hodgkin’s lymphoma (a rapid review using the software RobotReviewer). Cochrane Haematological Malignancies Group, editor. Cochrane Database Syst Rev [Internet]. 12 de julio de 2018 [citado 24 de mayo de 2022];2018(7). Disponible en: https://doi.wiley.com/10.1002/14651858.CD012556.pub274. Bilal J, Riaz IB, Kamal MU, Elyan M, Sudano D, Khan MA. 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J Clin Epidemiol. octubre de 2020;126:37-44.ORIGINAL2022_Tesis_Heider_Alexis_Bautista.pdf2022_Tesis_Heider_Alexis_Bautista.pdfTesisapplication/pdf2022094https://repository.unab.edu.co/bitstream/20.500.12749/17464/1/2022_Tesis_Heider_Alexis_Bautista.pdf37c87b7056e391bd8d353222ad63eab3MD51open access2022_Licencia_Heider_Alexis_Bautista.pdf2022_Licencia_Heider_Alexis_Bautista.pdfLicenciaapplication/pdf494790https://repository.unab.edu.co/bitstream/20.500.12749/17464/2/2022_Licencia_Heider_Alexis_Bautista.pdf280c745677a584452bfcffa64eab582bMD52metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/17464/3/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD53open accessTHUMBNAIL2022_Tesis_Heider_Alexis_Bautista.pdf.jpg2022_Tesis_Heider_Alexis_Bautista.pdf.jpgIM Thumbnailimage/jpeg4417https://repository.unab.edu.co/bitstream/20.500.12749/17464/4/2022_Tesis_Heider_Alexis_Bautista.pdf.jpgdc78ff700014b30ebb323471e9178253MD54open access2022_Licencia_Heider_Alexis_Bautista.pdf.jpg2022_Licencia_Heider_Alexis_Bautista.pdf.jpgIM Thumbnailimage/jpeg12357https://repository.unab.edu.co/bitstream/20.500.12749/17464/5/2022_Licencia_Heider_Alexis_Bautista.pdf.jpgc65efee3443cf317e8df3aecad819128MD55metadata only access20.500.12749/17464oai:repository.unab.edu.co:20.500.12749/174642023-11-21 21:54:38.85open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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