Abstract：
OBJECTIVE: The aim of this study is to determine if supervised machine learning algorithms can accurately predict voided computerized physician order entry in oral and maxillofacial surgery inpatients.
METHODS: Data from Electronic Medical Record included patient demographics, comorbidities, procedures, vital signs, laboratory values, and medication orders were retrospectively collected. Predictor variables included patient demographics, comorbidities, procedures, vital signs, and laboratory values. Outcome of interest is if a medication order was voided or not. Data was cleaned and processed using Microsoft Excel and Python v3.12. Gradient Boosted Decision Trees, Random Forest, K-Nearest Neighbor, and Naïve Bayes were trained, validated, and tested for accuracy of the prediction of voided medication orders.
RESULTS: 37,493 medication orders from 1,204 patient admissions over 5 years were used for this study. 3,892 (10.4%) medication orders were voided. Gradient Boosted Decision Trees, Random Forest, K-Nearest Neighbor, and Naïve Bayes had an Area Under the Receiver Operating Curve of 0.802 with 95% CI [0.787, 0.825], 0.746 with 95% CI [0.722, 0.765], 0.685 with 95% CI [0.667, 0.699], and 0.505 with 95% CI [0.489, 0.539], respectively. Area Under the Precision Recall Curve was 0.684 with 95% CI [0.679, 0.702], 0.647 with 95% CI [0.638, 0.664], 0.429 with 95% CI [0.417, 0.434], and 0.551 with 95% CI [0.551, 0.552], respectively.
CONCLUSIONS: Gradient Boosted Decision Trees was the best performing model of the supervised machine learning algorithms with satisfactory outcomes in the test cohort for predicting voided Computerized Physician Order Entry in Oral and Maxillofacial Surgery inpatients.
METHODS: Data from Electronic Medical Record included patient demographics, comorbidities, procedures, vital signs, laboratory values, and medication orders were retrospectively collected. Predictor variables included patient demographics, comorbidities, procedures, vital signs, and laboratory values. Outcome of interest is if a medication order was voided or not. Data was cleaned and processed using Microsoft Excel and Python v3.12. Gradient Boosted Decision Trees, Random Forest, K-Nearest Neighbor, and Naïve Bayes were trained, validated, and tested for accuracy of the prediction of voided medication orders.
RESULTS: 37,493 medication orders from 1,204 patient admissions over 5 years were used for this study. 3,892 (10.4%) medication orders were voided. Gradient Boosted Decision Trees, Random Forest, K-Nearest Neighbor, and Naïve Bayes had an Area Under the Receiver Operating Curve of 0.802 with 95% CI [0.787, 0.825], 0.746 with 95% CI [0.722, 0.765], 0.685 with 95% CI [0.667, 0.699], and 0.505 with 95% CI [0.489, 0.539], respectively. Area Under the Precision Recall Curve was 0.684 with 95% CI [0.679, 0.702], 0.647 with 95% CI [0.638, 0.664], 0.429 with 95% CI [0.417, 0.434], and 0.551 with 95% CI [0.551, 0.552], respectively.
CONCLUSIONS: Gradient Boosted Decision Trees was the best performing model of the supervised machine learning algorithms with satisfactory outcomes in the test cohort for predicting voided Computerized Physician Order Entry in Oral and Maxillofacial Surgery inpatients.
摘要：
目的：这项研究的目的是确定有监督的机器学习算法是否可以准确地预测口腔颌面外科住院患者中排泄的计算机化医师订单输入。
方法：来自电子病历的数据包括患者人口统计学，合并症,程序,生命体征,实验室值，并对用药单进行回顾性收集。预测变量包括患者人口统计学，合并症,程序,生命体征,和实验室值。感兴趣的结果是药物订单是否被作废。使用MicrosoftExcel和Pythonv3.12清理和处理数据。梯度提升决策树，随机森林,K-最近的邻居,和朴素贝叶斯训练，已验证，并测试了排泄药物订单预测的准确性。
结果：这项研究使用了来自1,204名患者入院5年的37,493份药物订单。3,892份（10.4%）的用药订单被作废。梯度提升决策树，随机森林,K-最近的邻居,朴素贝叶斯的接收器工作曲线下面积为0.802，95%CI[0.787，0.825]，0.746，95%CI[0.722，0.765]，0.685，95%CI[0.667，0.699]，和0.505，95%CI[0.489，0.539]，分别。精确召回曲线下面积为0.684，95%CI[0.679,0.702]，0.647，95%CI[0.638，0.664]，0.429，95%CI[0.417，0.434]，和0.551，95%CI[0.551，0.552]，分别。
结论：梯度增强决策树是监督机器学习算法中表现最好的模型，在预测口腔颌面外科住院患者的空计算机医师医嘱输入的测试队列中具有令人满意的结果。
方法：来自电子病历的数据包括患者人口统计学，合并症,程序,生命体征,实验室值，并对用药单进行回顾性收集。预测变量包括患者人口统计学，合并症,程序,生命体征,和实验室值。感兴趣的结果是药物订单是否被作废。使用MicrosoftExcel和Pythonv3.12清理和处理数据。梯度提升决策树，随机森林,K-最近的邻居,和朴素贝叶斯训练，已验证，并测试了排泄药物订单预测的准确性。
结果：这项研究使用了来自1,204名患者入院5年的37,493份药物订单。3,892份（10.4%）的用药订单被作废。梯度提升决策树，随机森林,K-最近的邻居,朴素贝叶斯的接收器工作曲线下面积为0.802，95%CI[0.787，0.825]，0.746，95%CI[0.722，0.765]，0.685，95%CI[0.667，0.699]，和0.505，95%CI[0.489，0.539]，分别。精确召回曲线下面积为0.684，95%CI[0.679,0.702]，0.647，95%CI[0.638，0.664]，0.429，95%CI[0.417，0.434]，和0.551，95%CI[0.551，0.552]，分别。
结论：梯度增强决策树是监督机器学习算法中表现最好的模型，在预测口腔颌面外科住院患者的空计算机医师医嘱输入的测试队列中具有令人满意的结果。
