Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology
The accuracy of the results is essential to evaluate the effectiveness of statistical methods in the analysis of medical data with uncertainty. Indicators such as margin of error, percent agreement and coefficient of determination quantified accuracy under epistemic and ontological uncertainty. The...
- Autores:
-
Nieto Sanchez, Zulmary Carolina
Bravo Valero, Antonio José
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/16224
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/16224
https://doi.org/10.56294/saludcyt20241341
- Palabra clave:
- Estadística
Métodos computacionales
Datos imprecisos
Incertidumbre
Epistemología
Ontología
Statistics
Computational Methods
Imprecise Data
Uncertainty
Epistemology
Ontology
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivs 3.0 United States
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Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology |
| dc.title.translated.spa.fl_str_mv |
Exploración de métodos computacionales en el análisis estadístico de datos médicos imprecisos: entre epistemología y ontología |
| title |
Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology |
| spellingShingle |
Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology Estadística Métodos computacionales Datos imprecisos Incertidumbre Epistemología Ontología Statistics Computational Methods Imprecise Data Uncertainty Epistemology Ontology |
| title_short |
Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology |
| title_full |
Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology |
| title_fullStr |
Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology |
| title_full_unstemmed |
Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology |
| title_sort |
Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology |
| dc.creator.fl_str_mv |
Nieto Sanchez, Zulmary Carolina Bravo Valero, Antonio José |
| dc.contributor.author.none.fl_str_mv |
Nieto Sanchez, Zulmary Carolina Bravo Valero, Antonio José |
| dc.subject.spa.fl_str_mv |
Estadística Métodos computacionales Datos imprecisos Incertidumbre Epistemología Ontología |
| topic |
Estadística Métodos computacionales Datos imprecisos Incertidumbre Epistemología Ontología Statistics Computational Methods Imprecise Data Uncertainty Epistemology Ontology |
| dc.subject.keywords.eng.fl_str_mv |
Statistics Computational Methods Imprecise Data Uncertainty Epistemology Ontology |
| description |
The accuracy of the results is essential to evaluate the effectiveness of statistical methods in the analysis of medical data with uncertainty. Indicators such as margin of error, percent agreement and coefficient of determination quantified accuracy under epistemic and ontological uncertainty. The stability of the methods was assessed by variation in trend analysis, sensitivity to small variations and model robustness. Data reliability focused on the selection of methods that effectively handle epistemic uncertainty, recording assumptions, sensitivity analysis and internal consistency. Ontological imprecision was quantified using the fuzzy membership degree and the overlap coefficient. The exploration of computational methods underlined the importance of accuracy and the handling of epistemic and ontological uncertainty, ensuring reliable results. The geometric mean filter, with a score of 0,7790, stood out as the best for its accuracy and ability to effectively handle uncertainty. |
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2024 |
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2024 |
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2025-02-06T15:43:00Z |
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2025-02-06T15:43:00Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo científico |
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Nieto Sánchez, Z. C., & Bravo Valero, A. J. (2024). Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology. Salud, Ciencia Y Tecnología, 4, 1341. https://doi.org/10.56294/saludcyt20241341 |
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27699711 |
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https://hdl.handle.net/20.500.12442/16224 |
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https://doi.org/10.56294/saludcyt20241341 |
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Nieto Sánchez, Z. C., & Bravo Valero, A. J. (2024). Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology. Salud, Ciencia Y Tecnología, 4, 1341. https://doi.org/10.56294/saludcyt20241341 27699711 |
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https://hdl.handle.net/20.500.12442/16224 https://doi.org/10.56294/saludcyt20241341 |
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eng |
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Nieto Sanchez, Zulmary Carolinad63e51e8-679e-4a5f-be34-68e1e3a7232d600Bravo Valero, Antonio José40b505eb-4254-496a-85c8-3fe5f7e2658d6002025-02-06T15:43:00Z2025-02-06T15:43:00Z2024Nieto Sánchez, Z. C., & Bravo Valero, A. J. (2024). Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology. Salud, Ciencia Y Tecnología, 4, 1341. https://doi.org/10.56294/saludcyt2024134127699711https://hdl.handle.net/20.500.12442/16224https://doi.org/10.56294/saludcyt20241341The accuracy of the results is essential to evaluate the effectiveness of statistical methods in the analysis of medical data with uncertainty. Indicators such as margin of error, percent agreement and coefficient of determination quantified accuracy under epistemic and ontological uncertainty. The stability of the methods was assessed by variation in trend analysis, sensitivity to small variations and model robustness. Data reliability focused on the selection of methods that effectively handle epistemic uncertainty, recording assumptions, sensitivity analysis and internal consistency. Ontological imprecision was quantified using the fuzzy membership degree and the overlap coefficient. The exploration of computational methods underlined the importance of accuracy and the handling of epistemic and ontological uncertainty, ensuring reliable results. The geometric mean filter, with a score of 0,7790, stood out as the best for its accuracy and ability to effectively handle uncertainty.La exactitud de los resultados es esencial para evaluar la eficacia de métodos estadísticos en el análisis de datos médicos con incertidumbre. Indicadores como el margen de error, el porcentaje de concordancia y el coeficiente de determinación cuantificaron la precisión bajo incertidumbre epistémica y ontológica. La estabilidad de los métodos se evaluó mediante la variación en análisis de tendencias, la sensibilidad a pequeñas variaciones y la robustez del modelo. La confiabilidad de los datos se centró en la selección de métodos que manejan eficazmente la incertidumbre epistémica, registrando supuestos, análisis de sensibilidad y consistencia interna. La imprecisión ontológica se cuantificó mediante el grado de pertenencia difuso y el coeficiente de solapamiento. La exploración de métodos computacionales subrayó la importancia de la precisión y el manejo de la incertidumbre epistémica y ontológica, asegurando resultados fiables. El filtro de media geométrica, con una puntuación de 0,7790, destacó como el mejor por su precisión y capacidad para el manejo eficaz de la incertidumbre.pdfengAG EditorAttribution-NonCommercial-NoDerivs 3.0 United Stateshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Salud, Ciencia y TecnologíaVol. 4 (2024)EstadísticaMétodos computacionalesDatos imprecisosIncertidumbreEpistemologíaOntologíaStatisticsComputational MethodsImprecise DataUncertaintyEpistemologyOntologyExploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontologyExploración de métodos computacionales en el análisis estadístico de datos médicos imprecisos: entre epistemología y ontologíainfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Maghrabie, H., Beauregard, Y., & Schiffauerova, A. Grey-based Multi-Criteria Decision Analysis approach: Addressing uncertainty at complex decision problems. Technological Forecasting and Social Change. 2019; 146:366-379. https://doi.org/10.1016/J.TECHFORE.2019.05.031Keith, A., & Ahner, D. A survey of decision making and optimization under uncertainty. Annals of Operations Research. 2019; 300:319-353. https://doi.org/10.1007/s10479-019-03431-8Hewitt, M., Ortmann, J., & Rei, W. Decision-based scenario clustering for decision-making under uncertainty. Annals of Operations Research. 2021;1-25. https://doi.org/10.1007/s10479-020-03843-xMoroni, S., & Chiffi, D. Uncertainty and Planning: Cities, Technologies and Public Decision-Making. Perspectives on Science. 2022; 30:237-259. https://doi.org/10.1162/posc_a_00413Hinkel, J., Feyen, L., Hemer, M., Cozannet, G., Lincke, D., Marcos, M., Mentaschi, L., Merkens, J., Moel, H., Muis, S., Nicholls, R., Vafeidis, A., Wal, R., Vousdoukas, M., Wahl, T., Ward, P., & Wolff, C. Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk. Earth's Future. 2021;9. https://doi.org/10.1029/2020EF001882Ng, S., Faraji-Rad, A., & Batra, R. Uncertainty Evokes Consumers’ Preference for Brands Incongruent with their Global–Local Citizenship Identity. Journal of Marketing Research. 2020; 58:400-415. https://doi.org/10.1177/0022243720972956Herran, D., Tachiiri, K., & Matsumoto, K. Global energy system transformations in mitigation scenarios considering climate uncertainties. Applied Energy. 2019; 243:119-131. https://doi.org/10.1016/J.APENERGY.2019.03.069Stefan, A. Statistics for Making Decisions. The American Statistician. 2022; 76:87-88. https://doi.org/10.1080/00031305.2021.2020003Roman-Acosta D, Rodríguez-Torres E, Baquedano-Montoya MB, López-Zavala L, Pérez-Gamboa AJ. ChatGPT y su uso para perfeccionar la escritura académica en educandos de posgrado. Praxis Pedagógica. 2024;24(36):53-75. https://revistas.uniminuto.edu/index.php/praxis/article/view/3536Hassani, H., Beneki, C., Silva, E., Vandeput, N., & Madsen, D. The science of statistics versus data science: What is the future? Technological Forecasting and Social Change. 2021; 173:121111. https://doi.org/10.1016/J.TECHFORE.2021.121111Chicco D, Shiradkar R. Ten quick tips for computational analysis of medical images. PLOS Computational Biology. 2023;19. https://doi.org/10.1371/journal.pcbi.1010778Barisoni L, Lafata K, Hewitt S, Madabhushi A, Balis U. Digital pathology and computational image analysis in nephropathology. Nature Reviews Nephrology. 2020;16:669-685. https://doi.org/10.1038/s41581-020-0321-6Sengupta K, Srivastava P. Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients. BMC Medical Informatics and Decision Making. 2021;21. https://doi.org/10.1186/s12911-021-01588-6Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019;9:1303-1322. https://doi.org/10.7150/thno.30309Abdar M, Pourpanah F, Hussain S, Rezazadegan D, Liu L, Ghavamzadeh M, et al. A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges. Information Fusion. 2020;76:243-297. https://doi.org/10.1016/j.inffus.2021.05.008Wang X, Yao L, Wang X, Paik H, Wang S. Uncertainty Estimation With Neural Processes for Meta-Continual Learning. IEEE Transactions on Neural Networks and Learning Systems. 2022;34:6887-6897. https://doi.org/10.1109/TNNLS.2022.3215633Al-turjman F, Zahmatkesh H, Mostarda L. Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning. IEEE Access. 2019;7:115749-115759. https://doi.org/10.1109/ACCESS.2019.2931637Herzog L, Murina E, Dürr O, Wegener S, Sick B. Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical image analysis. 2020;65:101790. https://doi.org/10.1016/j.media.2020.101790Ghesu F, Georgescu B, Mansoor A, Yoo Y, Gibson E, Vishwanath R, et al. Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment. Medical image analysis. 2020; 68:101855. https://doi.org/10.1016/j.media.2020.101855Rajaraman S, Zamzmi G, Yang F, Xue Z, Jaeger S, Antani S. Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays. Biomedicines. 2022;10. https://doi.org/10.3390/biomedicines10061323Sathiyamoorthi V, Ilavarasi A, Murugeswari K, Ahmed S, Devi B, Kalipindi M. A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images. Measurement. 2020; 171:108838. https://doi.org/10.1016/j.measurement.2020.108838Cabeli V, Verny L, Sella N, Uguzzoni G, Verny M, Isambert H. Learning clinical networks from medical records based on information estimates in mixed-type data. PLoS Computational Biology. 2020;16. https://doi.org/10.1371/journal.pcbi.1007866Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications. 2019; 32:18069-18083. https://doi.org/10.1007/s00521-019-04051-wSifaou H, Kammoun A, Alouini M. High-Dimensional Quadratic Discriminant Analysis Under Spiked Covariance Model. IEEE Access. 2020;8:117313-117323. https://doi.org/10.1109/ACCESS.2020.3004812Ikotun A, Ezugwu E, Abualigah L, Abuhaija B, Heming J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences. 2023;622(C):178–210. https://doi.org/10.1016/j.ins.2022.11.139Dembińska A, Jasiński K. Maximum likelihood estimators based on discrete component lifetimes of a k-out-of-n system. TEST. 2020; 30:407-428. https://doi.org/10.1007/s11749-020-00724-0Correia S, Guimarães P, Zylkin T. Fast Poisson estimation with high-dimensional fixed effects. The Stata Journal. 2019; 20:115-95. https://doi.org/10.1177/1536867X20909691Mannam V, Zhang Y, Zhu Y, Nichols E, Wang Q, Sundaresan V, Zhang S, Smith C, Bohn PW, Howard SS. Real-time image denoising of mixed Poisson-Gaussian noise in fluorescence microscopy images using Image. Optica. 2022;9(4):335-345. https://doi.org/10.1364/OPTICA.448287Navya B, Sridevi J, Vasanth K. Modified Geometric Mean as an Estimator of Outlier based Artifacts in Natural Images. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). Trichy, India: IEEE; 2022. p. 1095–102. https://doi.org/10.1109/ICOSEC54921.2022.9951924Vera M, Bravo A, Medina R. Description and use of three-dimensional numerical phantoms of cardiac computed tomography images. Data. 2022;7(8):115. https://doi.org/10.3390/data7080115Li X, Chen W, Li F, Kang R. Reliability evaluation with limited and censored time-to-failure data based on uncertainty distributions. Applied Mathematical Modelling. 2021;94:403-420. https://doi.org/10.1016/J.APM.2021.01.029Grzegorzewski P, Romaniuk M. Bootstrap Methods for Epistemic Fuzzy Data. International Journal of Applied Mathematics and Computer Science. 2022;32:285-297. https://doi.org/10.34768/amcs-2022-0021Derbyshire J. Answers to questions on uncertainty in geography: Old lessons and new scenario tools. Environment and Planning A: Economy and Space. 2019;52:710-727. https://doi.org/10.1177/0308518X19877885Asim Shahid M, Alam M, Mohd Su'ud M. Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm. PloS one. 2023;18(4):e0284209. https://doi.org/10.1371/journal.pone.0284209Schober P, Mascha E, Vetter T. Statistics From A (Agreement) to Z (z Score): A Guide to Interpreting Common Measures of Association, Agreement, Diagnostic Accuracy, Effect Size, Heterogeneity, and Reliability in Medical Research. Anesthesia and analgesia. 2021;133(6):1633-1641. https://doi.org/10.1213/ANE.0000000000005773Sánchez-González J, Rocha-de-Lossada C, Flikier D. Median absolute error and interquartile range as criteria of success against the percentage of eyes within a refractive target in IOL surgery. Journal of Cataract & Refractive Surgery. 2020;46(10):1441. https://doi.org/10.1097/j.jcrs.0000000000000248Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021;7:e623. https://doi.org/10.7717/peerj-cs.623Sarker IH. (2021). Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science. 2021;2(5):377. https://doi.org/10.1007/s42979-021-00765-8Antoniano-Villalobos I, Borgonovo E, Lu X. Nonparametric estimation of probabilistic sensitivity measures. Statistics and Computing. 2019; 30:447-467. https://doi.org/10.1007/s11222-019-09887-9Lee M, Khoo M, Chew X, Then P. Effect of Measurement Errors on the Performance of Coefficient of Variation Chart With Short Production Runs. IEEE Access. 2020; 8:72216-72228. https://doi.org/10.1109/ACCESS.2020.2985410Ortiz-Pimiento N, Díaz-Serna F. (2019). Relative average deviation as measure of robustness in the stochastic project scheduling problem. Revista Facultad de Ingeniería. 2019;28(52):77-97. https://doi.org/10.19053/01211129.v28.n52.2019.9756Starczewski J, Goetzen P, Napoli C. Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems. Journal of Artificial Intelligence and Soft Computing Research. 2020; 10:271-285. https://doi.org/10.2478/jaiscr-2020-0018Shoaip N, El-Sappagh S, Abuhmed T, Elmogy M. A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning. Scientific reports. 2024;14(1):4275. https://doi.org/10.1038/s41598-024-54065-1Eaton JW, Bateman D, Hauberg S, Wehbring R. GNU Octave version 5.1.0 manual: A high-level interactive language for numerical computations. 2019. https://www.gnu.org/software/octave/doc/interpreterLi G, Yang L, Lee C, Wang X, Rong M. A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties. IEEE Transactions on Industrial Electronics. 2021;68:8829-8841. https://doi.org/10.1109/TIE.2020.3009593Chen S, Zhang Q, Zhang T, Zhang L, Peng L, Wang S. Robust State Estimation With Maximum Correntropy Rotating Geometric Unscented Kalman Filter. IEEE Transactions on Instrumentation and Measurement. 2022;71:1-14. https://doi.org/10.1109/TIM.2021.3137553Cao B, Zhao J, Liu X, Arabas J, Tanveer M, Singh A, Lv Z. Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming. IEEE Transactions on Fuzzy Systems. 2022;30:4190-4200. https://doi.org/10.1109/TFUZZ.2022.3141761Kubíček J, Strycek M, Cerný M, Penhaker M, Prokop O, Vilimek D. Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System. Sensors. 2021;21. https://doi.org/10.3390/s21124161ARABI H, Zaidi H. Non-local mean denoising using multiple PET reconstructions. Annals of Nuclear Medicine. 2020;35:176-186. https://doi.org/10.1007/s12149-020-01550-yMeng Z, Pang Y, Pu Y, Wang X. New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties. Computer Methods in Applied Mechanics and Engineering. 2020;363:112886. https://doi.org/10.1016/j.cma.2020.112886Hüllermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Machine Learning. 2019;110:457-506. https://doi.org/10.1007/s10994-021-05946-3Afanador Cubillos N. Historia de la producción y sus retos en la era actual. Región Científica. 2023;2(1):202315. https://doi.org/10.58763/rc202315Kammerer-David MI, Murgas-Téllez B. La innovación tecnológica desde un enfoque de dinámica de sistemas. Región Científica. 2024;3(1):2024217. https://doi.org/10.58763/rc2024217Muñoz Bonilla HA, Menassa Garrido IS, Rojas Coronado L, Espinosa Rodríguez MA. La innovación en el sector servicios y su relación compleja con la supervivencia empresarial. 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