ARDS early dynamic early warning method and system based on conventional noninvasive parameters

Abstract

The invention discloses an ARDS early dynamic early warning method and system based on conventional non-invasive parameters. The method comprises the steps: preprocessing the non-invasive parameters of a patient, preprocessing the non-invasive parameters, and constructing feature data; standardizing the feature data to generate standardized feature data; inputting the standardized feature data into a pre-trained intelligent dynamic early warning model to obtain an original probability; calculating a corrected early warning probability; and when the corrected early warning probability of the patient is greater than or equal to 0.5, judging that the patient can obtain ARDS at the time. According to the method, only common noninvasive parameters are used, laboratory data are not needed, the application range of the method is expanded, the method can be used in remote areas, public health emergencies and battlefield frontline conditions, harm to individual patients caused by frequent collection of laboratory parameters is eliminated, the use cost is reduced, and the method can provide better ARDS morbidity early-warning capability, preserve early-warning intervals for clinical intervention, and provide sufficient time for doctors to design patient treatment schedules.

Description

Technical field [0001] The present invention relates to the field of artificial intelligence technology and medical health, and in particular, an early dynamic warning method and system based on conventional non-invasive parameters. Background technique [0002] Acute respiratory distress syndrome (ACUTE RESPIRATORY DISTRESS SYNDROME) is a clinical syndrome caused by multiple acute stimuli such as hypervisor, sepsis, trauma, shock and diffuse vascular coagulation. It is mainly based on the clinical manifestation of ketzophilia based on refractory. ARDS disease is characterized by high prevalence and high mortality. 10% of all ICU patients occur, 23% of all mechanical ventilation patients occur, ARDS is more urgent, can be 24-48 hours Can you grow to 5 to 7 days. Although ARDS's mortality is decreasing in clinical trials, in major observational studies, mortality remains around 40%. [0003] Some studies have shown that protective mechanical ventilation, input liquid restrictions,...

AI Technical Summary

Problems solved by technology

First of all, although the underlying algorithms of these methods are all public, in practice some risk adjustment algorithms contain laboratory data, and the acquisition of these parameters will result in high medical costs, and it is even more difficult to obtain in remote areas

In addition, although chest x-rays are permitted for early stage diagnosis of ARDS, they expose patients to radiation doses

Early detection of ARDS can be achieved with airway aspiration or lavage, the disadvantage of these methods is that they are invasive and harmful to the patient

All of the above methods are not suitable for frequent verification of whether patients suffer from ARDS or may develop ARDS; second, these algorithms do not reserve enough time for clinical intervention between the learning window and the early warning window (onset time), and cannot Better meet the actual clinical needs

Third, the traditional risk adjustment algorithm is limited by static low-frequency data, ignoring a large amount of information contained in dynamic time series data, the accuracy of early warning is low, and the guiding significance of early warning results is not strong

Fourth, these methods usually require medical staff to frequently record and calibrate patient data. The scoring of these algorithms often relies on the medical experience and clinical observations of medical staff, which is highly subjective and requires a large amount of medical human resources. It also increases the difficulty of applying the risk adjustment algorithm

Patent CN111407250A discloses a system for monitoring the development of ARDS in patients, but the system input parameters include respiratory mechanics parameters, pulse index continuous cardiac output, central venous pressure and pulmonary artery pressure, oxygen metabolism power, end-tidal carbon dioxide, etc. It is difficult to obtain hardware support in public health emergencies, frontline battlefields or remote areas, etc.

[0005] Although the above patents can realize the possible prediction of the patient's ARDS onset through the patient's physiological parameters, these are not designed for the whole-process monitoring of the patient's ARDS dynamic early warning, lack of difficulty in data collection, time series data mining, reserved early warning model running time point and onset Clinical intervention time between early warning time points, consideration of the possibility of dynamic monitoring throughout the hospitalization of patients

At present, there is no patent related to the dynamic early warning system for acute respiratory distress syndrome based on conventional non-invasive parameters

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