2023
DOI: 10.3389/fsurg.2022.1068321
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Identifying an optimal machine learning model generated circulating biomarker to predict chronic postoperative pain in patients undergoing hepatectomy

Abstract: Chronic postsurgical pain (CPSP) after hepatectomy is highly prevalent and challenging to treat. Several risk factors have been unmasked for CPSP after hepatectomy, such as acute postoperative pain. The current secondary analysis of a clinical study sought to extend previous research by investigating more clinical variables and inflammatory biomarkers as risk factors for CPSP after hepatectomy and sifting those strongly related to CPSP to build a reliable machine learning model to predict CPSP occurring. Parti… Show more

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Cited by 3 publications
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“…The current development advanced by integrating additional factors like preoperative sensory phenotyping, or other multifeature perioperative biomarker (Table 1). Using different statistical methods (e.g., logistic regression, generalized linear mixed model, ordinal regression), which recently include Machine Learning/ Artificial Intelligence approaches [30 ▪ ,31] (e.g., Support Vector Machine models, Gradient Boosted Decision Trees, XGBoost algorithm) prognostic models are developed from various selected factors (Fig. 1).…”
Section: Development Of Prognostic And/or Predictive Models For Chron...mentioning
confidence: 99%
“…The current development advanced by integrating additional factors like preoperative sensory phenotyping, or other multifeature perioperative biomarker (Table 1). Using different statistical methods (e.g., logistic regression, generalized linear mixed model, ordinal regression), which recently include Machine Learning/ Artificial Intelligence approaches [30 ▪ ,31] (e.g., Support Vector Machine models, Gradient Boosted Decision Trees, XGBoost algorithm) prognostic models are developed from various selected factors (Fig. 1).…”
Section: Development Of Prognostic And/or Predictive Models For Chron...mentioning
confidence: 99%