Modelos de progresión de la Enfermedad Renal Crónica: Revisión Sistemática
Palabras clave:
Modelos de Progresión, enfermedad renal crónicaResumen
Introducción: La enfermedad renal crónica (ERC) representa una carga para la salud mundial, con un rango estimado de 5 a 10 millones de muertes anuales a nivel mundial. El objetivo de esta investigación es identificar, evaluar y resumir los hallazgos de los modelos de predicción utilizados para determinar la progresión de la ERC y sus principales características.
Método: Se desarrolló una revisión sistemática que sigue las directrices PRISMA y se adhiere a las metodologías propuestas por Kitchenham la que permite identificar y mapear la evidencia existente e investigar y determinar las lagunas de conocimiento en torno al tema y sigue un enfoque estandarizado para buscar, filtrar e informar artículos.
Resultados: La búsqueda inicial arrojó un total de 429 artículos en Medline, Crochane y Scopus, se eliminaron 23 duplicados, 321 fueron excluidos según los criterios preestablecidos. Se evaluó la elegibilidad de texto completo de 85 artículos y posteriormente se mantuvieron 37 artículos que se incluyeron en la revisión cualitativa final.
Discusión: En general utilizan variables clínicas como creatinina sérica, tasa de filtración glomerular estimada (eTFG), edad, peso, comorbilidades y medicación, integradas mediante técnicas de aprendizaje supervisado, regresión de riesgos proporcionales de Cox, regresión logística y redes neuronales artificiales. Conclusiones: La regresión de Cox es el modelo predominante para predecir la progresión de la ERC. Solo un pequeño número de estudios utilizó modelos de aprendizaje automático, IA u otras herramientas. También se identificaron disímiles métodos para validar el modelo, aunque el que se utilizó con mayor frecuencia fue la curva ROC.
Descargas
Citas
1. Gallardo LS, Rodríguez AJ, Burgos M, Martínez ML, et al. Utilidad de un modelo de predicción para la enfermedad renal crónica en una unidad de primer nivel de atención. 2016. Nefrología; 36(2): 89-216. Disponible en: DOI: 10.1016/j.nefro.2015.10.010.
2. Lim DKE, Boyd JH, Thomas E, Chakera A, Tippaya S, et al. Prediction models used in the Progression of chronic kidney disease: A scoping review. 2022. PLOS ONE; 17(7). Disponible en: https://doi.org/10.1371/journal.pone.0271619.
3. Reddy S, Roy S, Choy KW, Sharma S, Dwyer KM et al. Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models. Computer Methods and Programs in Biomedicine Update 6. 2024 Disponible en: https://doi.org/10.1016/j.cmpbup.2024.100160.
4. Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: refer- ence and alternative scenarios for 2016–40 for 195 countries and territories. Lancet. 2018; 392 (10159):2052–90. Epub 2018/10/21. PMID: 30340847; PubMed Central PMCID: PMC6227505. Disponible en: https://doi.org/10.1016/S0140-6736(18)31694-5
5. Obrador GT, Schultheiss UT, Kretzler M, Langham RG, Nangaku M, Pecoits-Filho R, et al. Genetic and environmental risk factors for chronic kidney disease. Kidney international supplements. 2017; 7(2):88– 106. Epub 2017/09/20. Disponible en: https://doi.org/10.1016/j.kisu.2017.07.004
6. Chartier MJ, Tangri N, Komenda P, Walld R, Koseva I, Burchill C, et al. Prevalence, socio-demographic characteristics, and comorbid health conditions in pre-dialysis chronic kidney disease: Results from the Manitoba chronic kidney disease cohort. BMC Nephrology. 2018; 19(1). Disponible en: https://doi.org/10.1186/ s12882-018-1058-3 PMID: 30305038
7. Levin A, Tonelli M, Bonventre J, Coresh J, Donner J-A, Fogo AB, et al. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. The Lancet. 2017; 390(10105):1888– 917. Disponible en: https://doi.org/10.1016/S0140-6736(17)30788-2.
8. Wong LY, Liew AST, Weng WT, Lim CK, Vathsala A, Toh MPHS. Projecting the Burden of Chronic Kidney Disease in a Developed Country and Its Implications on Public Health. International Journal of Nephrology. 2018:1–9. Disponible en: https://doi.org/10.1155/2018/5196285
9. Lopez-Vargas PA, Tong A, Phoon RKS, Chadban SJ, Shen Y, Craig JC. Knowledge deficit of patients with stage 1–4 CKD: A focus group study. Nephrology. 2014; 19(4):234–43. Disponible en: https://doi.org/10.1111/ nep.12206
10. Escalona-González SO, González- Milán ZC. Inteligencia artificial en la predicción de mortalidad por enfermedad renal crónica. Anales de la ACC [online]. 2022, vol.12, n.3 [citado 2025-05-19]. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_ arttext&pid=S2304-01062022000300004 &lng=es &nrm=iso>. Epub 01-Nov-2022. ISSN 2304-0106.
11. Ferguson T, Ravani P, Sood MM, Clarke A, et al. Development and external validation of a machine learning model for progression of CKD, Kidney Int Rep. 2022; 7(8): 1772-1781. Publicado 2022 may 13. Disponible en: doi:10.1016/j.ekir.2022.05.004.
12. Olczak J, Pavlopoulos J, Prijs J, et al. Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop. 2021; 92(5): 513–525. Disponible en: doi:10.1080/17453674.2021.1918389.
13. Hernández Y; Barrio L. ¿Se puede predecir la progresión de la enfermedad renal crónica con unos datos básicos demográficos y de laboratorio? Vol. 2. Núm. 4.agosto 2011. Suplemento Extrardinario de Nefrología Basada en la Evidencia, Páginas 7-76. Disponible en: DOI: 10.3265/NefrologiaSuplementoExtraordinario.pre 2011.Jul.11044
14. Jessica Ivonne Bravo Zuñiga, Percy Soto Becerra, Edgar Juan Coila Paricahua, Jorge Ricardo Chávez Gómez, et al. Recalibración del modelo KFRE de 4 variables para predecir falla renal en población adulta con enfermedad renal crónica de ESSALUD. Reporte de resultados de investigación 01-2024. Instituto de Evaluación de Tecnologías En Salud E Investigación. Disponible en: https://repositorio.es salud.gob.pe/bitstream/handle/ 20.500.12959/5027/RRI-01-2024.pdf?sequence=1&is Allowed=y
15. Van den Brand JAJG, Dijkstra TMH, Wetzels J, Stengel B, Metzger M, Blankestijn PJ, et al. Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models. PLoS ONE. 2019;14(5):e0216559
16. Hallan SI, Rifkin DE, Potok OA, Katz R, Langlo KA, Bansal N, et al. Implementing the European Renal Best Practice Guidelines suggests that prediction equations work well to differentiate risk of end-stage renal disease vs. death in older patients with low estimated glomerular filtration rate. Kidney Int. septiembre de 2019;96(3):728-37.
17. Bravo-Zúñiga J, Chávez-Gómez R, Soto-Becerra P. Multicentre external validation of the prognostic model kidney failure risk equation in patients with CKD stages 3 and 4 in Peru: a retrospective cohort study. BMJ Open. 1 de enero de 2024;14(1):e076217.
18. Lilia Susana Gallardo Vidala, , Adriana Jheny Rodríguez Méndezb, Mariana Burgos Ochoac, Martha Leticia Martínez Martínezb, Pablo García Solísa, et al. Utilidad de un modelo de predicción para la enfermedad renal crónica en una unidad de primer nivel de atención. Nefrololgía, Vol. 36. Núm. 2.marzo - abril 2016 Páginas 89-216. Disponible en: DOI: 10.1016/j.nefro.2015.10.010
19. David K E Lim1,*, James H Boyd1,2, Elizabeth Thomas1,3, Aron Chakera3,4, Sawitchaya Tippaya5, Ashley Irish6, et al. Prediction models used in the progression of chronic kidney disease: A scoping review. PLoS One. 26 de julio de 2022;17(7):e0271619. Disponible en: doi: 10.1371/journal.pone.0271619
20. Tangri N, Inker LA, Hiebert B, Wong J, Naimark D, Kent D, et al. Un modelo predictivo dinámico para la progresión de la enfermedad renal crónica (ERC). Revista estadounidense de enfermedades renales: la revista oficial de la Fundación Nacional del Riñón. 2016;69(4):514–20. Disponible en: doi: 10.1053/j.ajkd.2016.07.030
21. Akbari A, Tangri N, Brown PA, Biyani M, Rhodes E, Kumar T, et al. Predicción de la progresión de la enfermedad renal poliquística mediante la ecuación de riesgo de insuficiencia renal y parámetros ecográficos. Revista Canadiense de Salud y Enfermedad Renal. 2020;7. Disponible en: doi: 10.1177/2054358120911274
22. Escalona González, SO; González Milán, ZC. Inteligencia artificial en la predicción de mortalidad por enfermedad renal crónica. Anales de la ACC [online]. 2022, vol.12, n.3 [citado 2025-05-19]. Disponible en: <http://scielo.sld.cu/scielo.php?script=sci_arttext &pid=S2304-01062022000300004 &lng=es &nrm=iso>. Epub 01-Nov-2022. ISSN 2304-0106.
23. S. Reddy, S Roy, KW Choy, S Sharma, KM Dwyer et al. Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models. Computer Methods and Programs in Biomedicine Update 6 (2024) 100160. Disponible en: https://doi.org/10.1016/j.cmpbup.2024.100160
24. T. Ferguson, P. Ravani, M.M. Sood, A. Clarke, P. Komenda, C. Rigatto, et al., Development and external validation of a machine learning model for progression of CKD, Kidney Int Rep 7 (8) (2022) 1772–1781.
25. J. Olczak, J. Pavlopoulos, J. Prijs, F.F.A. Ijpma, J.N. Doornberg, C. Lundstrom, et al., Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal, Acta Orthop 92 (5) (2021) 513–525.
26. S. Reddy, J. Fox, M.P. Purohit, Artificial intelligence-enabled healthcare delivery, J. R. Soc. Med. 112 (1) (2019) 22–28.
27. S. Reddy, Explainability and artificial intelligence in medicine, Lancet Digit Health 4 (4) (2022) e214–e2e5.
28. A. Ebbehoj, M. Thunbo, O.E. Andersen, M.V. Glindtvad, A. Hulman, Transfer learning for non-image data in clinical research: a scoping review, PLOS Digit Health 1 (2) (2022) e0000014.
29. L. Chan, G.N. Nadkarni, F. Fleming, J.R. McCullough, P. Connolly, G. Mosoyan, et al., Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease, Diabetologia 64 (7) (2021) 1504–1515.
30. Jialin Liu, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu, Ke Li. Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model. PLoS One 4 de febrero de 2021;16(2):e0246306. Disponible en: doi: 10.1371/journal.pone.0246306
31. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine. 2018; 169(7):467–73. Disponible en: https://doi.org/10.7326/m18-0850 PMID: 30178033
32. Kitchenham B., Brereton P.: A systematic review of systematic review process research in software engineering, Information and Software Technology, 55:(12), pp. 2049-2075, (2013).
33. Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology. 2018; 18(1):143. Disponible en: https://doi.org/10.1186/s12874-018-0611-x
34. Lim DKE, Boyd JH, Thomas E, Chakera A, Tippaya S, Irish A, et al. (2022) Prediction models used in the progression of chronic kidney disease: A scoping review. PLoS ONE 17(7): e0271619. Disponible en: https://doi.org/10.1371/journal.pone.0271619
35. Kim HW, Park JT, Joo YS, Kang SC, Lee JY, Lee S, et al. Systolic blood pressure and chronic kidney disease progression in patients with primary glomerular disease. Journal of Nephrology. 2021; 34 (4):1057–67. Disponible en: https://doi.org/10.1007/s40620-020-00930-x
36. Min-Kuang Tsai; WayneGao; Kuo-Liong Chien; Chih-Cheng Hsu; Chi-Pang Wen. A prediction model with lifestyle factors improves the predictive ability for renal replacement therapy: a cohort of 442 714 Asian adults. Clinical Kidney Journal, 2022, vol. 15, no. 10, 1896–1907. Disponible en: https:/doi.org/10.1093/ckj/sfac119
37. Dai D, Alvarez PJ, Woods SD. A predictive model for progression of chronic kidney disease to kidney failure using a large administrative claims database. ClinicoEconomics and Outcomes Research. 2021; 13:475–86. Disponible en: https://doi.org/10.2147/CEOR.S313857
38. Li L, Luo S, Hu B, Greene T. Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease. Statistics in Biosciences. 2017; 9(2):357–78. Disponible en: https://doi.org/ 10.1007/s12561-016-9183-7
39. Palant C, Chawla L, Faselis C, Li P, Kimmel PL, Amdur R. The association of serum creatinine variabil- ity and progression to ckd. Nephrology Dialysis Transplantation. 2015; 30(SUPPL. 3). Disponible en: http://dx.doi.org/ 10.1093/ndt/gfv175.4.
40. Zhao J, Gu S, McDermaid A. Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression. Mathematical Biosciences. 2019; 310:24–30. Disponible en: https://doi.org/10.1016/j. mbs.2019.02.001
41. Zhou F, Gillespie A, Gligorijevic D, Gligorijevic J, Obradovic Z. Use of disease embedding technique to predict the risk of progression to end-stage renal disease. Journal of Biomedical Informatics. 2020; 105: N.PAG-N.PAG. Disponible en: https://doi.org/10.1016/j.jbi.2020.103409 .
42. Esra Kürüm; Brian Kwan; Qi Qian; Sudipto Banerjee; Connie M. Rhee; Danh V. Nguyen; Damla Şentürk. A Bayesian Joint Model of Longitudinal Kidney Disease Progression, Recurrent Cardiovascular Events, and Terminal Event in Patients with Chronic Kidney Disease. Statistics in Biosciences (2025) 17:528–554 Disponible en: https://doi.org/10.1007/s12561-024-09429-6
43. Chang HL, Wu CC, Lee SP, Chen YK, Su W, Su SL. A predictive model for progression of CKD. Medi- cine (United States). 2019; 98(26):e16186. Disponible en: https://doi.org/10.1097/MD.0000000000016186 PMID: 31261555
44. Cheng LC, Hu YH, Chiou SH. Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression. Journal of Medical Systems. 2017; 41(5):85. https://doi.org/10.1007/ s10916-017-0732-5 PMID: 28401396
45. Min-Kuang Tsai; Wayne Gao; Kuo-Liong Chien; Chih-Cheng Hsu; Chi-Pang Wen; et al. A prediction model with lifestyle factors improves the predictive ability for renal replacement therapy: a cohort of 442 714 Asian adults. Clinical Kidney Journal, 2022, vol. 15, no. 10, 1896–1907. Disponible en: https:/doi.org/10.1093/ckj/sfac119
46. Chen-Mao Liao; Yi-Wei Kao; Yi-Ping Chang; and hih-Ming Lin. An Approach for Personalized Dynamic Assessment of Chronic Kidney Disease Progression Using Joint Model. Biomedicines 2024, 12, 622. Disponible en: https://doi.org/10.3390/biomedicines12030622
47. Yufeng Liang QL; Zhenhuan Zou; Binsan Huang; Nan Zhong; Chenlun Li. A Dynamic Prediction Model for Renal Progression in Primary Membranous Nephropathy. Int. J. Med. Sci. 2024; 21(7): 1292-1301. Disponible en: doi: 10.7150/ijms.95321
48. Akbari A, Tangri N, Brown PA, Biyani M, Rhodes E, Kumar T, et al. Prediction of Progression in Polycys- tic Kidney Disease Using the Kidney Failure Risk Equation and Ultrasound Parameters. Canadian Jour- nal of Kidney Health and Disease. 2020; 7. Disponible en: https://doi.org/10.1177/2054358120911274
49. Tangri N, Inker LA, Hiebert B, Wong J, Naimark D, Kent D, et al. A Dynamic Predictive Model for Pro- gression of CKD. American journal of kidney diseases: the official journal of the National Kidney Foun- dation. 2016; 69(4):514–20. Disponible en: https://doi.org/10.1053/j.ajkd.2016.07.030 PMID: 27693260
50. Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, et al. A predictive model for pro- gression of chronic kidney disease to kidney failure. JAMA. 2011; 305(15):1553–9. Disponible en: https://doi.org/10. 1001/jama.2011.451
51. Huda Al-Wahsh; Navdeep Tangri; Rob Quinn; PingLiu; Thomas Ferguson; Marta Fiocco; Ngan N. Lam; Marcello Tonelli; Pietro Ravani. Accounting for the Competing Risk of Death to Predict Kidney Failure in Adults With Stage 4 Chronic Kidney Disease. JAMA Network Open. 2021;4(5):e219225. Disponible en: doi:10.1001/ jamanetworkopen.2021.9225
52. Navdeep Tangri; Thomas Ferguson; Silvia J. Leon; Stefan D. Anke; Gerasimos Filippatos. Validation of the Klinrisk chronic kidney disease progression model in the FIDELITY population. Clinical Kidney Journal, 2024, vol. 17, no. 4, sfae052. Disponible en: https:/doi.org/10.1093/ckj/sfae052
53. Hasegawa T, Sakamaki K, Koiwa F, Akizawa T. Clinical prediction models for progression of chronic kidney disease to end stage kidney failure under predialysis nephrology care: Results from the chronic kidney disease Japan cohort study. Nephrology Dialysis Transplantation. 2016; 31(SUPPL. 1). Disponible en: http:// dx.doi.org/10.1093/ndt/gfw189.2.
54. Xu Q, Wang Y, Fang Y, Feng S, Chen C, Jiang Y. An easy-to-operate web-based calculator for predict- ing the progression of chronic kidney disease. Journal of Translational Medicine. 2021; 19(1) (no pagi- nation). Disponible en: https://doi.org/10.1186/s12967-021-02942-y PMID: 34217324
55. Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T, Koseki A, et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Scientific reports. 2019; 9 (1):11862. Disponible en: https://doi.org/10.1038/s41598-019-48263-5 PMID: 31413285
56. Toyoshi Inoguchi; Tasuku Okui; Chinatsu Nojiri; Erina Eto; Nao Hasuzawa; et al. A simplifed prediction model for end‑stage kidney disease in patients with diabetes. Scientifc Reports (2022) 2:12482. Disponible en: https://doi.org/10.1038/s41598-022-16451-5
57. Kang MW, Tangri N, Kim YC, An JN, Lee J, Li L, et al. An independent validation of the kidney failure risk equation in an Asian population. Scientific reports. 2020; 10(1):12920. Disponible en: https://doi.org/10.1038/ s41598-020-69715-3
58. Kim HW, Park JT, Joo YS, Kang SC, Lee JY, Lee S, et al. Systolic blood pressure and chronic kidney disease progression in patients with primary glomerular disease. Journal of Nephrology. 2021; 34 (4):1057–67. Disponible en: https://doi.org/10.1007/s40620-020-00930-x
59. Zacharias HU, Altenbuchinger M, Schultheiss UT, Samol C, Kotsis F, Poguntke I, et al. A Novel Meta- bolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease. Journal of proteome research. 2019; 18(4):1796–805. Disponible en: https://doi.org/10.1021/acs. jproteome.8b00983
60. Cornec-Le Gall E, Audrezet M-P, Rousseau A, Hourmant M, Renaudineau E, Charasse C, et al. The PROPKD Score: A New Algorithm to Predict Renal Survival in Autosomal Dominant Polycystic Kidney Disease. Journal of the American Society of Nephrology: JASN. 2016; 27(3):942–51. Disponible en: https://doi.org/10. 1681/ASN.2015010016
61. Diggle PJ, Sousa I, Asar O. Real-time monitoring of progression towards renal failure in primary care patients. Biostatistics (Oxford, England). 2015; 16(3):522–36. Disponible en: https://doi.org/10.1093/biostatistics/ kxu053
62. Norouzi J, Yadollahpour A, Mirbagheri SA, Mazdeh MM, Hosseini SA. Predicting Renal Failure Progres- sion in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System. Computational and mathematical methods in medicine. 2016; 2016:6080814. Disponible en: https://doi.org/10.1155/2016/6080814
63. Nastasa A, Apetrii M, Onofriescu M, Nistor I, Hussien H, Popa C, et al. Risk prediction for death and end-stage renal disease does not parallel real-life trajectory of older patients with advanced chronic kid- ney disease-a Romanian center experience. Nephrology Dialysis Transplantation. 2020; 35(SUPPL 3). Disponible en: http://dx.doi.org/10.1093/ndt/gfaa142.P0184.
64. Mrara B, Paruk F, Sewani-Rusike C, et al. Development and validation of a clinical prediction model of acute kidney injury in intensive care unit patients at a rural tertiary teaching hospital in South Africa: a study protocol. BMJ Open 2022;12:e060788. Disponible en: doi:10.1136/ bmjopen-2022-060788
65. Dovgan E, Gradisek A, Lustrek M, Uddin M, Nursetyo AA, Annavarajula SK, et al. Using machine learn- ing models to predict the initiation of renal replacement therapy among chronic kidney disease patients. PloS one. 2020; 15(6):e0233976. Disponible en: https://doi.org/10.1371/journal.pone.0233976
66. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learn-ing in Python. Journal of Machine Learning Research. 2011; 12:2825–2830.
67. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2016. p. 785–794.
68. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York; 2009.
69. Breiman L. Random forests. Machine Learning. 2001; 45(1):5–32. Disponible en: https://doi.org/10.1023/A:1010933404324
70. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995; 20(3):273–297. Disponible en: https://doi.org/10.1023/A:1022627411411
71. Shalev-Shwartz S, Singer Y, Srebro N, Cotter A. Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming. 2011; 127(1):3–30. Disponible en: https://doi.org/10.1007/s10107-010-0420-4
72. Goldberger J, Hinton GE, Roweis ST, Salakhutdinov RR. Neighbourhood components analysis. In: Advances in Neural Information Processing Systems; 2005. p. 513–520.
73. Zhang H. The Optimality of Naïve Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004. 2004; p. 6 pages.
74. Fitzmaurice G, Laird N. Multivariate Analysis: Discrete Variables (Logistic Regression). In: International Encyclopedia of the Social & Behavioral Sciences. Elsevier; 2001. p. 10221–10228.
75. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553):436–444. Disponible en: https://doi.org/10.1038/nature14539
Descargas
Publicado
Cómo citar
Número
Sección
Categorías
Licencia
Derechos de autor 2025 Dania Castillo Deprés, Saymara Castillo Deprés, Ariel Delgado Ramos, María Josefina Vidal Ledo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
