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Desarrollo de un modelo matemático para la predicción del pronóstico de pacientes con tormenta de citoquinas inducida por COVID-19 mediante técnicas de aprendizaje automático
Citation
Solera Navarro, Enrique (2023). Desarrollo de un modelo matemático para la predicción del pronóstico de pacientes con tormenta de citoquinas inducida por COVID-19 mediante técnicas de aprendizaje automático. Proyecto Fin de Carrera / Trabajo Fin de Grado, E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM), Madrid.
Description
| Title: | Desarrollo de un modelo matemático para la predicción del pronóstico de pacientes con tormenta de citoquinas inducida por COVID-19 mediante técnicas de aprendizaje automático |
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| Item Type: | Final Project |
| Degree: | Grado en Biotecnología |
| Date: | July 2023 |
| Subjects: | |
| Faculty: | E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM) |
| Department: | Biotecnología - Biología Vegetal |
| Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Abstract
ABSTRACT
This study presents a comprehensive analysis of cytokine storm in COVID-19 patients using a mathematical and computational approach. Patient data was collected and preprocessed to identify relevant variables and behavioral patterns. Innovative methods were applied in training and developing the mathematical model with the aim of predicting the prognosis of cytokine storm.
The prediction model was evaluated using two distinct strategies: numerical validation and validation using the RandomForestClassifier prediction model. A numerical convergence study was conducted by solving the model using three different methods: 4th order Runge-Kutta (RK4), Euler, and Heun.
The results demonstrated that the model aligns well with real data, providing a valuable tool for predicting the prognosis of cytokine storm in COVID-19 patients. However, certain limitations were identified, and suggestions for potential improvements in future studies were provided.
This work contributes to the understanding of cytokine storm in COVID-19 and establishes a solid foundation for further research in this field.
More information
| Item ID: | 76693 |
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| DC Identifier: | https://oa.upm.es/76693/ |
| OAI Identifier: | oai:oa.upm.es:76693 |
| Deposited by: | Biblioteca ETSI Agronómica, Alimentaria y de Biosistemas |
| Deposited on: | 20 Nov 2023 09:20 |
| Last Modified: | 20 Nov 2023 09:20 |
