Correlation detecting system for correlation between mechanical ventilation driving pressure and ventilator-related event

Abstract

The invention discloses a correlation detecting system for correlation between a mechanical ventilation driving pressure and a ventilator-related event. The system comprises the components of a data preprocessing module which obtains a mechanical ventilation driving pressure starting value, a final value and a mechanical ventilation driving pressure change value in 48 hours, and filling in an acquired to-be-tested case index; a characteristic selecting module which screens a pathological characteristic related with the ventilator-related event generation from ventilator-related event generation cases as a training set; and a model constructing and detecting module which constructs a correlation detecting module based on the training set by means of a logistic regression algorithm, detectsthe to-be-tested case index based on the correlation detecting model and determines a correlation probability between the mechanical ventilation driving pressure change value and the ventilator-related event generation. Based on the logistic regression algorithm of machine learning, the ventilator-related event (VAE) is correlated with the mechanical ventilation driving pressure change value, thereby monitoring the influence of the mechanical ventilation driving pressure change to the ventilator-related event (VAE).

Description

technical field [0001] The present disclosure relates to the technical field of medical data mining, in particular to a correlation detection system between mechanical ventilation driving pressure and ventilator-related events. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Mechanical ventilation is an essential life-saving therapy for critically ill and respiratory failure patients. Patients receiving mechanical ventilation may have complications after mechanical ventilation, such as ventilator-associated pneumonia (VAP), sepsis, acute respiratory distress syndrome (ARDS), pulmonary embolism, barotrauma, and pulmonary edema, so observe the driving pressure of mechanical ventilation. changes are very necessary. [0004] At present, it is difficult to diagnose VAE related to ventilator in a timely manner clinically, and the current dete...

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Problems solved by technology

[0004] At present, it is difficult to diagnose VAE related to ventilator in a timely manner clinically, and the current detection methods involve more subjective components, such as imaging, secretions, and auscultation, etc., and these indicators are not specific, resulting in the inconsistency of VAE detection methods, Confusion, etc., so objective alternatives to monitoring should be sought;

[0005] Moreover, according to the knowledge of the inventors, based on data mining and machine learning related methods, there has not been a detection system for exploring the correlation between the variation of mechanical ventilation driving pressure and the occurrence of ventilator-related events VAE

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