AU - Chin TY - JOUR AU - Chin, AU - Cheng Kuan-Chen, AU - Sun Yu-Chia AU - Ou, AU - en- yen Hu, AU - Tsai Chun-Hua, AU - chi Ma, Matthew hueiming AU - Chiang, AU -Chen Wen-Chu AU -Chen, Albert Y PY - 2022 DA - 2022/6/10 TI -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:模型开发和验证JO - J医疗Internet Res SP - e30210 VL - 24 IS - 6kw -紧急医疗服务KW -紧急医疗调度KW -调遣员KW -创伤KW -机器学习KW -频率逆文档频率KW -伯努利naïve贝叶斯AB -背景:院前设置中早期识别严重受伤的患者对于及时治疗和将患者运送到进一步的治疗设施是至关重要的。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。比较了调度员和PAMT模型在严重情况下的预测性能。 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. SN - 1438-8871 UR - //www.mybigtv.com/2022/6/e30210 UR - https://doi.org/10.2196/30210 UR - http://www.ncbi.nlm.nih.gov/pubmed/35687393 DO - 10.2196/30210 ID - info:doi/10.2196/30210 ER -