Abstract：
OBJECTIVE: To identify factors and indicators that affect chronic pain and pain relief, and to develop predictive models using machine learning.
METHODS: We analyzed the data of 67,028 outpatient cases and 11,310 valid samples with pain from a large retrospective cohort. We used decision tree, random forest, AdaBoost, neural network, and logistic regression to discover significant indicators and to predict pain and treatment relief.
RESULTS: The random forest model had the highest accuracy, F1 value, precision, and recall rates for predicting pain relief. The main factors affecting pain and treatment relief included body mass index, blood pressure, age, body temperature, heart rate, pulse, and neutrophil/lymphocyte × platelet ratio. The logistic regression model had high sensitivity and specificity for predicting pain occurrence.
CONCLUSIONS: Machine learning models can be used to analyze the risk factors and predictors of chronic pain and pain relief, and to provide personalized and evidence-based pain management.
METHODS: We analyzed the data of 67,028 outpatient cases and 11,310 valid samples with pain from a large retrospective cohort. We used decision tree, random forest, AdaBoost, neural network, and logistic regression to discover significant indicators and to predict pain and treatment relief.
RESULTS: The random forest model had the highest accuracy, F1 value, precision, and recall rates for predicting pain relief. The main factors affecting pain and treatment relief included body mass index, blood pressure, age, body temperature, heart rate, pulse, and neutrophil/lymphocyte × platelet ratio. The logistic regression model had high sensitivity and specificity for predicting pain occurrence.
CONCLUSIONS: Machine learning models can be used to analyze the risk factors and predictors of chronic pain and pain relief, and to provide personalized and evidence-based pain management.
摘要：
目的：确定影响慢性疼痛和疼痛缓解的因素和指标，并使用机器学习开发预测模型。
方法：我们分析了来自大型回顾性队列的67,028例门诊病例和11,310例有效疼痛样本的数据。我们用决策树,随机森林,AdaBoost,神经网络,和逻辑回归来发现重要指标并预测疼痛和治疗缓解。
结果：随机森林模型精度最高，F1值，精度,和预测疼痛缓解的召回率。影响疼痛和治疗缓解的主要因素包括体重指数，血压,年龄,体温,心率,脉搏,中性粒细胞/淋巴细胞×血小板比值。Logistic回归模型对预测疼痛发生具有较高的敏感性和特异性。
结论：机器学习模型可用于分析慢性疼痛和疼痛缓解的危险因素和预测因素，并提供个性化和循证的疼痛管理。
方法：我们分析了来自大型回顾性队列的67,028例门诊病例和11,310例有效疼痛样本的数据。我们用决策树,随机森林,AdaBoost,神经网络,和逻辑回归来发现重要指标并预测疼痛和治疗缓解。
结果：随机森林模型精度最高，F1值，精度,和预测疼痛缓解的召回率。影响疼痛和治疗缓解的主要因素包括体重指数，血压,年龄,体温,心率,脉搏,中性粒细胞/淋巴细胞×血小板比值。Logistic回归模型对预测疼痛发生具有较高的敏感性和特异性。
结论：机器学习模型可用于分析慢性疼痛和疼痛缓解的危险因素和预测因素，并提供个性化和循证的疼痛管理。
