Ventilation man-machine asynchronous detection model training method and device based on DQN reinforcement learning

Abstract

The invention relates to the field of artificial intelligence, in particular to a ventilation man-machine asynchronous detection model training method and device based on DQN reinforcement learning, and the method comprises the steps: obtaining a capacity data segment during breathing and ventilation; preprocessing the capacity data segment to obtain training data and test data which can be used for DQN reinforcement learning; based on the training data and the test data, constructing a scene problem suitable for applying DQN reinforcement learning processing; describing or depicting the scene problem; and constructing a specific DQN network, setting training parameters of a reinforcement learning model, and training a DQN reinforcement learning model. A man-machine asynchronous detection problem of breathing ventilation is converted into a scene problem, so that the man-machine asynchronous problem can be identified or detected by using a reinforcement learning scheme. A processing method of breathing ventilation man-machine asynchronous detection is expanded, the problem of automatic detection of man-machine asynchronous events occurring in the breathing ventilation process is solved, the monitoring burden is effectively relieved, and the nursing efficiency of work is improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a method and device for training a ventilation man-machine asynchronous detection model based on DQN enhanced learning. Background technique [0002] The ventilator is an important device that provides ventilation support when the patient's breathing ability is insufficient to meet the patient's own breathing needs for some reasons, and provides precious time for the treatment of the patient's primary disease. It is widely used in the intensive care department of the hospital , General departments, some ventilators have also entered the family, becoming some auxiliary household equipment for daily sleep, etc., providing important auxiliary support for people with respiratory dysfunction. [0003] Generally speaking, one of the most important functions of a ventilator is its ventilation sensitivity, whether it can provide the same frequency of air supply / ventilation support ...

AI Technical Summary

Problems solved by technology

These proposed models are trained in a supervised manner to obtain a trained model that recognizes the type of human-machine asynchrony. It is not difficult to see that the limit that this supervised scheme can achieve is to learn patterns in labeled data, the best theoretical performance of these supervised models is limited by the labeled data

In addition, these supervised learning-based models are also limited in the aforementioned personalization capabilities, lacking the ability of a model to self-explore new scenarios

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