Resumen
Se desarrolló un modelo predictivo basado en inteligencia artificial para identificar tempranamente complicaciones posquirúrgicas mediante indicadores clínicos y funcionales. Se analizaron 10.000 registros sintéticos del conjunto TERMINET mediante regresión logística, Random Forest y XGBoost, utilizando partición estratificada, validación cruzada y métricas de discriminación y calibración. Los modelos mostraron capacidad predictiva excepcional, con clasificación perfecta para regresión logística y Random Forest. El análisis SHAP identificó sodio, nitrógeno ureico, creatinina y parámetros relacionados con INR entre los principales predictores. Los resultados demuestran viabilidad computacional, aunque su elevado desempeño requiere interpretación cautelosa y validación externa posterior utilizando cohortes clínicas reales e independientes.
Citas
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[13] W. T. Stam, L. K. Goedknegt, E. W. Ingwersen, L. J. Schoonmade, E. R. Bruns, and F. Daams, “The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review,” Surgery, vol. 171, no. 4, pp. 1014–1021, 2022.
[14] T. J. Loftus, P. J. Tighe, A. C. Filiberto et al., “Artificial intelligence and surgical decision-making,” JAMA Surgery, vol. 155, no. 2, pp. 148–158, 2020.
[15] D. Lekka, A. Pnevmatikakis, E. Kanavos, B. Gottardelli, A. M. Tudor, P. Cornacchione, M. de Angeli, and A. Bellieni, “TERMINET eHealth post-operation complications synthetic dataset,” Zenodo, Dataset, 2024, doi: 10.5281/zenodo.10885957.
[2] K. Merath, J. M. Hyer, R. Mehta et al., “Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery,” Journal of Gastrointestinal Surgery, vol. 24, pp. 1843–1851, 2020, doi: 10.1007/s11605-019-04338-2.
[3] P. Thottakkara, T. Ozrazgat-Baslanti, B. B. Hupf, P. Rashidi, P. Pardalos, P. Momcilovic, and A. Bihorac, “Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications,” PLoS ONE, vol. 11, no. 5, Art. no. e0155705, 2016, doi: 10.1371/journal.pone.0155705.
[4] A. M. Hassan, A. Rajesh, M. Asaad, J. A. Nelson, J. H. Coert, B. J. Mehrara, and C. E. Butler, “Artificial intelligence and machine learning in prediction of surgical complications: Current state, applications, and implications,” The American Surgeon, vol. 89, no. 1, pp. 25–30, 2023.
[5] S. Kokkinakis, E. I. Kritsotakis, and K. Lasithiotakis, “Artificial intelligence in surgical risk prediction,” Journal of Clinical Medicine, vol. 12, no. 12, Art. no. 4016, 2023.
[6] C. Varghese, E. M. Harrison, G. O’Grady, and E. J. Topol, “Artificial intelligence in surgery,” Nature Medicine, vol. 30, no. 5, pp. 1257–1268, 2024, doi: 10.1038/s41591-024-02970-3.
[7] A. Guni, P. Varma, J. Zhang, M. Fehervari, and H. Ashrafian, “Artificial intelligence in surgery: The future is now,” European Surgical Research, vol. 65, no. 1, pp. 22–39, 2024.
[8] A. Bonde, K. M. Varadarajan, N. Bonde et al., “Assessing the utility of deep neural networks in predicting postoperative surgical complications: A retrospective study,” The Lancet Digital Health, vol. 3, no. 8, pp. e471–e485, 2021.
[9] S. Chen, T. Deng, Q. Yang et al., “Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: A machine learning study,” EClinicalMedicine, vol. 86, 2025.
[10] H. Deng, Z. Eftekhari, C. Carlin et al., “Development and validation of an explainable machine learning model for major complications after cytoreductive surgery,” JAMA Network Open, vol. 5, no. 5, Art. no. e2212930, 2022.
[11] P. Li, S. Gao, Y. Wang et al., “Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications,” British Journal of Anaesthesia, vol. 132, no. 6, pp. 1315–1326, 2024.
[12] N. Kenig, J. Monton Echeverria, and A. Muntaner Vives, “Artificial intelligence in surgery: A systematic review of use and validation,” Journal of Clinical Medicine, vol. 13, no. 23, Art. no. 7108, 2024.
[13] W. T. Stam, L. K. Goedknegt, E. W. Ingwersen, L. J. Schoonmade, E. R. Bruns, and F. Daams, “The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review,” Surgery, vol. 171, no. 4, pp. 1014–1021, 2022.
[14] T. J. Loftus, P. J. Tighe, A. C. Filiberto et al., “Artificial intelligence and surgical decision-making,” JAMA Surgery, vol. 155, no. 2, pp. 148–158, 2020.
[15] D. Lekka, A. Pnevmatikakis, E. Kanavos, B. Gottardelli, A. M. Tudor, P. Cornacchione, M. de Angeli, and A. Bellieni, “TERMINET eHealth post-operation complications synthetic dataset,” Zenodo, Dataset, 2024, doi: 10.5281/zenodo.10885957.
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