根据生命参数预测医疗干预：远程患者监护决策支持系统
Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring
摘要：
心血管疾病和心力衰竭是世界上非传染性疾病死亡的主要原因。持续的患者监测可以实现更好的医疗，因为它允许从业人员及时做出反应并提供适当的治疗。远程医疗可以提供持续的远程监测，因此患者可以留在家中，只需要医疗传感设备和网络连接。远程医疗中心的一个限制因素是可以同时监测的患者数量。我们的目标是通过实施决策支持系统来增加这一数额。本文研究了一种机器学习模型，用于根据患者的生命参数估计风险评分，该模型允许每天对所有病例进行分类，以帮助从业者将其有限的能力集中在最严重的病例上。我们提出的模型达到了0.84的AUCROC，而基于规则的基线模型达到了0.73的AUCROC。我们的研究结果表明，使用深度学习来提高远程医疗中心的效率是可行的。通过这种方式，更多的患者可以通过远程监控从更好的医疗保健中受益。
英文摘要：
Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment. Telemedicine can provide constant remote monitoring so patients can stay in their homes, only requiring medical sensing equipment and network connections. A limiting factor for telemedical centers is the amount of patients that can be monitored simultaneously. We aim to increase this amount by implementing a decision support system. This paper investigates a machine learning model to estimate a risk score based on patient vital parameters that allows sorting all cases every day to help practitioners focus their limited capacities on the most severe cases. The model we propose reaches an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of 0.73. Our results indicate that the usage of deep learning to improve the efficiency of telemedical centers is feasible. This way more patients could benefit from better health-care through remote monitoring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
