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采用机器学习方法建立Ⅰ类切口手术患者使用抗菌药物合理性的评价模型
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| 篇名： | 采用机器学习方法建立Ⅰ类切口手术患者使用抗菌药物合理性的评价模型 |
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| 摘要： | 目的：建立Ⅰ类切口手术患者使用抗菌药物合理性的评价模型，为临床药师进行处方点评提供依据。方法：以某院2017年1月1日－2017年12月31日住院的432例Ⅰ类切口手术患者作为研究对象，提取患者年龄、院内感染、使用抗菌药物的种类数等多项诊疗信息；结合临床药师对患者预防及治疗使用抗菌药物合理性的点评结果，以抗菌药物类别（预防或治疗使用）为因变量、患者诊疗信息为自变量，分别利用机器学习方法中的非条件Logistic回归法和支持向量机法，将药师点评结果转化为机器学习模型可识别的客观指标，建立Ⅰ类切口手术患者使用抗菌药物合理性的分类判别模型，并以灵敏度、特异度、约登指数等为指标，另采用61例Ⅰ类切口手术患者样本对建立的模型进行验证；收集使用模型前（人工点评，2017年1－12月）、后（2018年1－10月）Ⅰ类切口手术患者使用抗菌药物处方合理性点评情况，对模型效果进行应用评价。结果：以非条件Logistic回归法建立的模型的灵敏度为65.63％、特异度为75.00％、约登指数为40.63％；以支持向量机法建立的模型主要参数gamma为0.01、cost为10，其灵敏度为92.19％、特异度为87.50％、约登指数为79.69％，支持向量机法建立的模型优于非条件Logistic回归法；对采用支持向量机法建立的模型进行验证，结果灵敏度为100％、特异度为88.57％、约登指数为88.57％；与使用模型前比较，使用模型后处方点评比例由69.44％升高到100％，抗菌药物预防使用率由23.84％下降到16.43％，品种选用合理率由37.86％升高到54.39％，使用疗程由5.01 d缩短到3.26 d。结论：应用机器学习方法中的支持向量机法建立Ⅰ类切口手术患者使用抗菌药物的评价模型，可实现处方点评全覆盖，提高Ⅰ类切口手术患者使用抗菌药物的合理水平，同时为药师的处方点评工作提供了新思路。 |
| ABSTRACT： | OBJECTIVE： To establish antibiotics use rationality evaluation model in type Ⅰ incision surgery patients， and to provide reference for prescription review of clinical pharmacists. METHODS： Totally 432 inpatients underwent type Ⅰ surgical incision in a hospital from Jan. 1st- Dec. 31st， 2017 were selected as the research objects. The information of diagnosis and treatment including age， nosocomial infection， the number of kinds of antibiotics used were extracted. Based on the results of clinical pharmacists’ comments on the antibiotics use rationality in patients’ prevention and treatment， non-conditional Logistic regression and support vector machine （SVM） in machine learning method were used to convert clinical pharmacists’ comments into objective index that can be recognized by the machine learning model， using categories of antibiotics （preventive or therapeutic use） as dependent variables and the patient’s diagnosis and treatment information as independent variables. Classification and identification model was established for antibiotics use rationality in type Ⅰ incision surgery patients. Using sensitivity， specificity and Youden index as indexes， established mode was validated on the other 61 samples of type Ⅰ incision surgery patients. The rationality of antibiotics prescriptions in type Ⅰ incision surgery patients before （by manual review， Jan.-Dec. 2017） and after （Jan.-Oct. 2018） using the model were collected， and the effects of the model were evaluated. RESULTS： The sensitivity， specificity and Youden index of non-conditional Logistic regression model were 65.63％， 75.00％ and 40.63％， respectively. Main parameters of the model established by SVM included gamma 0.01， cost 10， sensitivity 92.19％， specificity 87.50％， Youden index 79.69％. The model established by SVM was better than non-conditional Logistic regression. SVM was used to validate established mode， and sensitivity， specificity and Youden index were 100％， 88.57％ and 88.57％， respectively. Compared with before using the model， the evaluation ratio increased from 69.44％ to 100％， the rate of prophylactic use of antibiotics decreased from 23.84％ to 16.43％， the rate of rational drug type selection increased from 37.86％ to 54.39％， and treatment course shortened from 5.01 days to 3.26 days after using the model. CONCLUSIONS： Established antibiotics use rationality evaluation model in typeⅠincision surgery patients by SVM in machine learning method fully covers all the patients， promotes rational use of antibiotics in typeⅠincision surgery patients， and provides a new idea for pharmacist prescription comment. |
| 期刊： | 2019年第30卷第9期 |
| 作者： | 朱立强，王勇敢，李卫华，苏庆军，白桂花，石德光，崔丽华 |
| AUTHORS： | ZHU Liqiang，WANG Yonggan，LI Weihua，SU Qingjun，BAI Guihua，SHI Deguang，CUI Lihua |
| 关键字： | 机器学习方法；非条件Logistic回归法；支持向量机法；评价模型；Ⅰ类切口手术患者；抗菌药物；处方点评；合理用药 |
| KEYWORDS： | Machine learning method； Non-conditional Logistic regression； Support vector machine； Evaluation model； TypeⅠincision surgery patients； Antibiotics； Prescription evaluation； Rational drug use |
| 阅读数： | 418 次 |
| 本月下载数： | 6 次 |
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