论文标题
一种预测机械通气患者的通风参数的深度学习方法
A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
论文作者
论文摘要
我们开发了一种深度学习方法，可以使用长期和短期记忆（LSTM）复发性神经网络（RNN）模型来预测机械通气患者的一组呼吸机参数。我们专注于对紧急重症监护病房（EICU）中化粪池患者一组呼吸机参数的短期预测。该模型的短期可预测性为主治医生提供了早期警告，以及时调整对EICU患者的治疗。可以对任何给定重症患者的患者进行特定的深度学习模型进行培训，这使其成为医生在紧急医疗情况下使用的智能助手。
We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model. We focus on short-term predictions of a set of ventilator parameters for the septic patient in emergency intensive care unit (EICU). The short-term predictability of the model provides attending physicians with early warnings to make timely adjustment to the treatment of the patient in the EICU. The patient specific deep learning model can be trained on any given critically ill patient, making it an intelligent aide for physicians to use in emergent medical situations.
