%0期刊文章%@ 1438-8871 %I JMIR出版物%V 24%卡塔尔世界杯8强波胆分析 N 6% P e30210% T基于机器学习的文本分析在紧急医疗调度中预测重伤患者:模型开发与验证%陈a，陈宽，郑a，孙玉嘉，陈振堂，欧志彦，胡志彦，蔡春华，蔡明志，马明志，蒋惠明，陈文柱，陈安杰，杨永强+台北市罗斯福路四段1号，台北市，886 2 3366 4255，AlbertChen@ntu.edu.tw %K紧急医疗服务%K紧急医疗调度%K调遣员%K创伤%K机器学习%K频率逆文档频率%K伯努利naïve贝叶斯%D 2022 %7 10.6.2022 %9原始论文%J J医学互联网Res %G英语%X背景:院前设置中早期识别重伤患者对于及时治疗和将患者运送到进一步的治疗设施至关重要。在以往的研究中，很少涉及调度精度问题。目的:在本研究中，我们旨在通过对紧急呼叫的文本挖掘，建立一个基于机器学习的模型，用于道路交通事故后重症患者的自动识别。方法:随机抽取台湾省台北市2018年交通事故录音资料。通话转移或非普通话演讲的数据被排除在外。为了预测紧急医疗技术人员在现场确定的严重创伤病例，所有纳入的病例都由人类(6名调度员)和机器学习模型(即院前激活的重大创伤(PAMT)模型)进行评估。PAMT模型使用词频-逆文档频率、基于规则的分类和伯努利naïve贝叶斯分类器开发。采用重复随机子抽样交叉验证方法评价模型的稳健性。 The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results: Although the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments. Conclusions: A machine learning–based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment. %M 35687393 %R 10.2196/30210 %U //www.mybigtv.com/2022/6/e30210 %U https://doi.org/10.2196/30210 %U http://www.ncbi.nlm.nih.gov/pubmed/35687393