论文标题
使用经常性和卷积神经网络将非结构化的语音和文本数据转换为护理人员紧急服务的见解
Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks
论文作者
论文摘要
医护人员通常必须在救护车的有限时间内做出救生决定。他们有时会向医生询问其他医疗指示，在此期间为患者带来了宝贵的时间。这项研究旨在自动融合语音和文本数据，以向护理人员提供量身定制的情境意识信息。为了训练和测试语音识别模型，我们建立了双向深度复发神经网络（长期记忆（LSTM））。然后，我们将卷积神经网络在定制训练的单词矢量之上用于句子级别的分类任务。每个句子自动分为四个类，包括患者状况，病史，治疗计划和药物提醒。随后，自动生成事件报告以提取关键字，并协助护理人员和医生做出决定。拟议的系统发现，它可以基于非结构化的语音和文本数据提供及时的药物通知，目前在护理人员紧急情况下是不可能的。此外，拟议系统提供的自动事件报告生成改善了常规但容易出错的护理人员和医生任务，帮助他们专注于患者护理。
Paramedics often have to make lifesaving decisions within a limited time in an ambulance. They sometimes ask the doctor for additional medical instructions, during which valuable time passes for the patient. This study aims to automatically fuse voice and text data to provide tailored situational awareness information to paramedics. To train and test speech recognition models, we built a bidirectional deep recurrent neural network (long short-term memory (LSTM)). Then we used convolutional neural networks on top of custom-trained word vectors for sentence-level classification tasks. Each sentence is automatically categorized into four classes, including patient status, medical history, treatment plan, and medication reminder. Subsequently, incident reports were automatically generated to extract keywords and assist paramedics and physicians in making decisions. The proposed system found that it could provide timely medication notifications based on unstructured voice and text data, which was not possible in paramedic emergencies at present. In addition, the automatic incident report generation provided by the proposed system improves the routine but error-prone tasks of paramedics and doctors, helping them focus on patient care.
