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Plataforma con sistema predictivo del IPC para los procesos de contratos, provisiones y presupuesto de equipos médicos en el Hospital Universitario San Ignacio

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Primary Garzón Carvajal., Valentina-2023.pdf (1.74 MB)
 Autorización.pdf (317.45 KB)
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Garzón, Valentina Carvajal
Abstract (Spanish)
Actualmente, el departamento de Gestión biomédica en el Hospital Universitario San Ignacio se encuentra asociado con más de 70 empresas nacionales e internacionales que los soportan en el desarrollo de su gestión tecnológica, con el fin de garantizar el mayor beneficio para usuarios internos y externos en cada uno de los 51 servicios que brinda la institución. Dicha gestión se relaciona directamente con el sistema de provisión y presupuesto de contratos, comodatos y órdenes de servicio, el cual se ha puesto en riesgo debido a los grandes volúmenes de datos con los que se cuenta, perjudicando al departamento en los ámbitos relacionados con el flujo de dinero frente a las provisiones agendadas para cada tipo de compromiso financiero, la notificación tardía de la renovación de contratos y comodatos; y el cálculo insuficiente o inexacto del presupuesto para cada año debido a variables como el índice de precios al consumidor (IPC). En búsqueda de solucionar los problemas anteriormente expuestos y lograr una correcta ejecución de los procesos de adquisición, monitorización, control de la tecnología, reposición y renovación, se propone el diseño e implementación de la herramienta o plataforma COPREV, la cual va a permitir unificar toda la información con respecto a contratos, comodatos, órdenes de servicio y sus correspondientes presupuestos y provisiones. COPREV contará con un sistema de notificación que permite resolver los problemas de suministro de dinero no agendado y la inadecuada renovación o formalización de un nuevo otrosí. También, mediante un modelo de predicción del IPC, preverá los ajustes que deban realizarse al presupuesto de cada proveedor.
Abstract (English)
Currently, the Biomedical Management Department at the San Ignacio University Hospital is associated with more than 70 national and international companies that support them in the development of their technological management, to guarantee the greatest benefit for internal and external users in each of the 51 services provided by the institution. This management is directly related to the system for the provision and budgeting of contracts, commodities, and service orders, which has been put at risk due to the large volumes of data available, harming the department in areas related to the flow of money against the provisions scheduled for each type of financial commitment, the late notification of the renewal of contracts and commodities, the late notification of the renewal of contracts and commodities and the late notification of the renewal of contracts and commodities; and insufficient or inaccurate calculation of the budget for each year due to variables such as the consumer price index (CPI). In order to solve the aforementioned problems, the design and implementation of the COPREV tool or platform is proposed, which allows unifying all the information regarding contracts, commodities, service orders and their corresponding budgets and provisions. COPREV will have a notification system that allows it to resolve problems with the supply of unscheduled money and the inadequate renewal or formalization of a new contract. Also, through a CPI prediction model, it will foresee the adjustments to be made to each supplier's budget.
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Tenjo, Camilo Antonio Castaño
Aya, Pedro Antonio Parra
Extent
39 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/2557
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BA - Trabajos Dirigidos de Biomédica
References

[1] “Historia - HUSI.” https://www.husi.org.co/historia (accessed Feb. 23, 2023). [2] “Índice de Precios al Consumidor (IPC).” https://www.dane.gov.co/index.php/estadisticas-por-tema/precios-y-costos/indice-de-precios-al-consumidor-ipc (accessed Feb. 23, 2023). [3] “Sistema de Gestión Integral - Almera.” https://almeraim.com/quienes-somos (accessed Apr. 13, 2023). [4] P. Chapman et al., “Step-by-step Data Mining Guide,” SPSS inc, vol. 78, pp. 1–78, 2000, [Online]. Available: https://www.semanticscholar.org/paper/CRISP-DM-1.0%3A-Step-by-step-data-mining-guide-Chapman-Clinton/54bad20bbc7938991bf34f86dde0babfbd2d5a72%0Ahttp://www.crisp-dm.org/CRISPWP-0800.pdf [5] “Índice de precios al consumidor (IPC) | Banco de la República.” https://www.banrep.gov.co/es/estadisticas/indice-precios-consumidor-ipc (accessed Apr. 30, 2023). [6] “DANE - IPC Nomenclatura basada en la COICOP.” https://www.dane.gov.co/index.php/estadisticas-por-tema/precios-y-costos/indice-de-precios-al-consumidor-ipc/ipc-actualizacion-metodologica-2019/ipc-nomenclatura-basada-en-la-coicop (accessed Apr. 30, 2023). [7] M. General, Í. D. E. Precios, A. L. Consumidor, and R. V. Ramirez, “Departamento Administrativo Nacional de Estadística Diseño DSO Dirección de Metodología y Producción Estadística / DIMPE,” 2019. [8] G. E. P. Box, G. C. Reinsel, and G. M. Jenkins, Time series analysis George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel Forecasting and control. 2016. [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&db=cat05358a&AN=crai.62847&site=eds-live [9] P.-Ning. A. Tan, Michael. Steinbach, Anuj. Karpatne, V. (1956-). Kumar, and Pearson. W. pbl, Introduction to data mining / Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. 2019. [Online]. Available: http://katalog.nukat.edu.pl/lib/item?id=chamo:4613659&theme=nukat [10] R. S. Tsay and R. Chen, “Nonlinear time series analysis,” Nonlinear Time Series Analysis, pp. 1–496, Jan. 2018, doi: 10.1002/9781119514312. [11] D. A. Petrusevich, “Review of missing values procession methods in time series data,” J Phys Conf Ser, vol. 1889, no. 3, p. 032009, Apr. 2021, doi: 10.1088/1742-6596/1889/3/032009. [12] D. B. Rubin, “Inference and Missing Data,” Biometrika, vol. 63, no. 3, pp. 581–592, 1976, doi: 10.2307/2335739. [13] N. Savarimuthu and S. Karesiddaiah, “An unsupervised neural network approach for imputation of missing values in univariate time series data,” Concurr Comput, vol. 33, no. 9, p. e6156, May 2021, doi: 10.1002/CPE.6156. [14] A. Conde, F. Salazar, and D. J. Vicente, “Tratamiento y análisis inteligente de datos del comportamiento de estructuras hidráulicas,” Jan. 2019, Accessed: May 02, 2023. [Online]. Available: https://www.scipedia.com/public/CONDE_et_al_2019a [15] Maciej Serda et al., “Estimación e imputación de datos faltantes mediante métodos de interpolación espacial para precipitación mensual acumulada en el departamento de Antioquia durante el periodo 2014-2018,” Uniwersytet śląski, vol. 7, no. 1, pp. 343–354, Apr. 2020, doi: 10.2/JQUERY.MIN.JS. [16] A. Almosova and N. Andresen, “Nonlinear inflation forecasting with recurrent neural networks,” J Forecast, vol. 42, no. 2, pp. 240–259, 2023, doi: 10.1002/for.2901. [17] L. Paranhos, “Predicting Inflation with Neural Networks,” no. November 2020, 2021, [Online]. Available: http://arxiv.org/abs/2104.03757 [18] M. A. Omar, Sara. E. Shahin, and M. Roshdy, “Forecasting Inflation in Egypt (2019-2022) by using AutoRegressive Integrated Moving Average (ARIMA) Models.,” J Adv Vet Res, vol. 12, no. 6, pp. 670–676, 2022, [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&db=edb&AN=162526063&site=eds-live [19] V. Karadzic and B. Pejovic, “Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models,” Amfiteatru Economic, vol. 23, no. 57. pp. 517–532, 2021. [Online]. Available: http://www.amfiteatrueconomic.ro/Arhiva_EN.aspx [20] R. J. Hyndman and G. Athanasopoulos Verfasser, Forecasting : principles and practice. 2021. [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&db=edsbvb&AN=edsbvb.BV047621999&site=eds-live [21] N. Absar, N. Uddin, M. U. Khandaker, and H. Ullah, “The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases,” Infect Dis Model, vol. 7, no. 1, p. 170, Mar. 2022, doi: 10.1016/J.IDM.2021.12.005. [22] L. Zhou, C. Zhao, N. Liu, X. Yao, and Z. Cheng, “Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach,” Eng Appl Artif Intell, vol. 122, p. 106157, Jun. 2023, doi: 10.1016/J.ENGAPPAI.2023.106157. [23] A. Géron, “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems.” 2019. [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2245240&site=eds-live [24] T. Chai, R. D.-G. model development, and undefined 2014, “Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature,” gmd.copernicus.org, vol. 7, pp. 1247–1250, 2014, doi: 10.5194/gmd-7-1247-2014. [25] J. Benesty, J. Chen, Y. Huang, and I. Cohen, “Noise reduction in speech processing,” 2009, Accessed: May 07, 2023. [Online]. Available: https://books.google.com/books?hl=es&lr=&id=mIjsifuDqrIC&oi=fnd&pg=PA1&ots=OFqo8CuKmb&sig=gqBHNuIPPVmw6Pb0lc8ia4_XwV4 [26] S. Kumari and S. K. Singh, “Machine learning-based time series models for effective CO(2) emission prediction in India.,” Environ Sci Pollut Res Int, Jul. 2022, doi: 10.1007/s11356-022-21723-8. [27] T. Wahyuningrum, G. F. Fitriana, A. C. Wardhana, M. A. Sidiq, and D. Wahyuningsih, “Developing Suicide Risk Idea Identification for Teenager (SERIINA) Mobile Apps Prototype using Extended Rapid Application Development,” 2021 9th International Conference on Information and Communication Technology, ICoICT 2021, pp. 92–97, Aug. 2021, doi: 10.1109/ICOICT52021.2021.9527508. [28] J. Knapp, J. Zeratsky, and B. Kowitz, Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days. in Business book summary. Simon \& Schuster, 2016. [Online]. Available: https://books.google.com.co/books?id=ICSpCwAAQBAJ [29] C. L. Vidal-Silva, A. Sánchez-Ortiz, J. Serrano, and J. M. Rubio, “Academic experience in rapid development of web information systems with Python and Django,” Formacion Universitaria, vol. 14, no. 5, pp. 85–94, 2021, doi: 10.4067/S0718-50062021000500085. [30] S. Dauzon, A. Bendoraitis, and A. Ravindran, “Django: Web Development with Python.” 2016. [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&db=e000xww&AN=1345264&site=eds-live [31] “Patrón MVT: Modelo-Vista-Template | Curso de Django | Hektor Profe.” https://docs.hektorprofe.net/django/web-personal/patron-mvt-modelo-vista-template/ (accessed May 09, 2023). [32] “Dash Documentation & User Guide | Plotly.” https://dash.plotly.com/ (accessed May 11, 2023). [33] C. Balzer, R. Oktavian, M. Zandi, D. Fairen-Jimenez, and P. Z. Moghadam, “Wiz: A Web-Based Tool for Interactive Visualization of Big Data,” Patterns, vol. 1, no. 8, p. 100107, Nov. 2020, doi: 10.1016/J.PATTER.2020.100107. [34] “Welcome to Flask — Flask Documentation (2.3.x).” https://flask.palletsprojects.com/en/2.3.x/ (accessed May 11, 2023). [35] “🌟�� Introducing Dash 🌟��. Create Reactive Web Apps in pure Python | by Plotly | Plotly | Medium.” https://medium.com/plotly/introducing-dash-5ecf7191b503 (accessed May 11, 2023). [36] “PostgreSQL: qué es, cómo funciona y ventajas.” https://platzi.com/blog/que-es-postgresql/ (accessed Jul. 12, 2023). [37] “Bootstrap · The most popular HTML, CSS, and JS library in the world.” https://getbootstrap.com/ (accessed May 11, 2023). [38] “Heurísticas de usabilidad de Nielsen | Bring Connections.” https://bringconnections.es/tipos-heuristicas-usabilidad-nielsen/ (accessed Jul. 12, 2023). [39] “3.2 Componentes de series temporales | Pronóstico: principios y práctica (3.a ed.).” https://otexts.com/fpp3/components.html (accessed May 18, 2023).