2026, Number S1
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Rev Mex Anest 2026; 49 (S1)
Predicting postoperative pain using deep learning
Nevárez-Prieto AO
Language: Spanish
References: 6
Page: 350-352
PDF size: 886.04 Kb.
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No abstract.
REFERENCES
Gan TJ. Poorly controlled postoperative pain: prevalence, consequences,and prevention. J Pain Res. 2017;10:2287-2298.
Tighe PJ, Harle CA, Hurley RW, Aytug H, Boezaart AP, FillingimRB. Teaching a machine to feel postoperative pain: combining highdimensionalclinical data with machine learning algorithms to forecastacute postoperative pain. Pain Med. 2015;16:1386-1401.
Schneller T, Cina A, Moroder P, Scheibel M, Lazaridou A. Using deeplearning to predict postoperative pain in reverse shoulder arthroplastypatients. JSES Int. 2025;9:748-755.
Fang J, Wu W, Liu J, Zhang S. Deep learning-guided postoperative painassessment in children. Pain. 2023;164:2029-2035.
Zhang C, He J, Liang X, Shi Q, Peng L, Wang S, et al. Deep learningmodels for the prediction of acute postoperative pain in PACU forvideo-assisted thoracoscopic surgery. BMC Med Res Methodol.2024;24:232.
Liu R, Gutiérrez R, Mather RV, Stone TAD, Santa Cruz MercadoLA, Bharadwaj K, et al. Development and prospective validationof postoperative pain prediction from preoperative EHR data usingattention-based set embeddings. NPJ Digit Med. 2023;6:209.
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