A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
This addresses a critical problem for physicians in emergency intensive care by offering an intelligent aide for patient-specific ventilator management, though it appears incremental as it applies an existing deep learning method to a specific medical domain.
The paper tackles predicting ventilator parameters for mechanically ventilated septic patients using an LSTM model, achieving short-term predictability to provide early warnings for timely treatment adjustments in emergency intensive care units.
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.