Abstract：
This study presents a deep learning model to predict phlebitis in patients with peripheral intravenous catheter (PIVC) insertions. Leveraging electronic health record data from 27,532 admissions and 70,293 PIVC events at a hospital in Seoul, South Korea, the study involved analyzing patient demographics, PIVC-specific features, and drug-related information. The developed deep learning model was benchmarked against various machine learning models, demonstrating superior performance with an accuracy of 0.93 and an AUC of 0.89. This highlights its potential as an effective tool for early detection of phlebitis, promising enhanced patient outcomes and healthcare efficiency.
摘要：
这项研究提出了一种深度学习模型来预测周围静脉导管（PIVC）插入患者的静脉炎。利用首尔一家医院的27,532例入院和70,293例PIVC事件的电子健康记录数据，韩国,这项研究涉及分析患者的人口统计学，PIVC特定的功能，与毒品有关的信息。开发的深度学习模型以各种机器学习模型为基准，表现出优异的性能，准确度为0.93，AUC为0.89。这凸显了其作为早期发现静脉炎的有效工具的潜力，有希望提高患者的治疗效果和医疗效率。
