Machine Learning Model for Predictive Ventilator Weaning
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Solution Overview
Problem
Current methods for weaning ventilated patients from mechanical ventilation are subjective and inaccurate, leading to prolonged ICU stays, increased incidence of Ventilator Associated Events, and higher mortality, due to variability in physician predictions and lack of objectivity in extubation decision-making.
Innovation Solution
A computer-enabled system that utilizes machine learning models trained on ventilator-recorded parameters to predict extubation candidacy and adjust ventilator settings, providing objective and standardized predictions for weaning and extubation, with features such as dynamic compliance, peak airway pressure, and ventilator work of breathing being key indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physician predictions are used to determine extubation candidacy, then clinical judgment can be applied, but accuracy and objectivity are low due to subjective variability
Solution Approach 1:
The patent replaces the mechanical system of human clinical judgment with an automated machine learning model that processes ventilator data. The model substitutes physician subjectivity with algorithmic objectivity, using trained predictors to determine extubation candidacy based on quantitative analysis of ventilation parameters rather than human interpretation
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw ventilator data and extubation decisions. This intermediary layer processes the data through trained predictors, providing a standardized interpretation that bridges the gap between complex ventilator parameters and clinically actionable extubation recommendations
2Reliability
If mechanical ventilation is prolonged to ensure patient stability, then patient safety is maintained, but negative outcomes increase including Ventilator Associated Events and mortality
Solution Approach 1:
The patent performs preliminary identification of extubation candidates using machine learning predictors before actual extubation occurs. By analyzing ventilator data patterns in advance, the system identifies patients who are ready for extubation, allowing clinicians to transition patients off ventilation at the optimal time rather than waiting for traditional clinical criteria to be met
Solution Approach 2:
The patent implements a feedback mechanism where machine learning models continuously analyze ventilator data and provide predictions about extubation candidacy. This feedback loop allows for dynamic adjustment of ventilation duration based on real-time patient status, enabling timely extubation decisions that balance safety with the need to reduce ventilation-associated harms
3Ease of operation
If traditional weaning protocols are used with clinical interventions, then patient care is provided, but duration of ventilation is increased leading to higher healthcare utilization and costs
Solution Approach 1:
The patent replaces traditional manual weaning protocols with an automated machine learning system that continuously monitors ventilator data and provides extubation recommendations. This substitution eliminates the time-consuming nature of traditional protocols by providing automated, real-time assessments of extubation readiness
Solution Approach 2:
The patent enables continuous analysis of ventilator data through the machine learning model, providing ongoing assessment of extubation candidacy without interruption. This continuous monitoring allows for timely identification of extubation opportunities, reducing unnecessary ventilation duration while maintaining appropriate care
Data Source
AI summary
The disclosed system and method generates a trained prediction model based on a plurality of sets of sampled ventilation parameter values received from patient ventilations, and a plurality of weaning indicators representative of patient outcomes for each sampled patient ventilation. Ventilation parameter values are sampled during a current patient ventilation and input into the trained prediction model. The model selects, from the group of ventilation parameters, a ventilation parameter and associated parameter value or range of parameter values having the highest probability of positively influencing the current patient ventilation based on a threshold value of the ventilator parameter. The system may then use the returned parameter value(s) to cause an operational mode of a ventilator associated with the current patient ventilation to be adjusted.