NIV Ventilation Tracking With ML Quality Scoring and Alerts
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Solution Overview
Problem
Existing non-invasive ventilation (NIV) systems require human intervention for patient monitoring, making large-scale remote monitoring challenging and difficult to implement effectively.
Innovation Solution
A system comprising a medical ventilator and computer server that processes telemetry data to determine a ventilation quality score using statistical indicators and machine learning, generating alerts and displaying patient data without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human intervention is used for patient monitoring, then treatment quality can be assessed, but operational complexity increases and large-scale implementation becomes difficult
Solution Approach 1:
The system enables self-monitoring by automatically collecting telemetry data from the ventilator, processing it through machine learning models, and generating ventilation quality scores without requiring human intervention. The system serves itself by performing data collection, analysis, and alert generation autonomously.
Solution Approach 2:
The patent replaces manual human assessment with an automated computer-based system that uses machine learning algorithms to evaluate ventilation quality. The mechanical/manual process of human review is substituted with electronic data processing and algorithmic analysis.
2Productivity
If remote monitoring is implemented without human intervention, then scalability improves, but the ability to accurately assess treatment quality may deteriorate
Solution Approach 1:
The system continuously monitors ventilation variables, compares them against predicted thresholds derived from machine learning, and provides feedback through ventilation quality scores and alerts. This closed-loop feedback mechanism enables accurate assessment while maintaining scalability.
Solution Approach 2:
The system transforms raw telemetry data into standardized ventilation quality scores by applying statistical indicators and machine learning models. This parameter transformation enables accurate quality assessment across large populations while maintaining scalability through automated processing.
3Loss of information
If multiple ventilation variables are collected and analyzed, then monitoring comprehensiveness improves, but data processing complexity increases
Solution Approach 1:
The system extracts and processes multiple ventilation variables (compliance, mask leaks, AHI, respiratory rate, spontaneous breathing rate, tidal volume) from the telemetry data. By systematically extracting each variable and analyzing it through statistical indicators, the system maintains comprehensiveness while managing complexity through structured processing.
Solution Approach 2:
The data processing is segmented into distinct steps: collecting ventilation variables, calculating statistical indicators (mean, standard deviation, skewness, kurtosis, trend), comparing against thresholds, and generating scores. This segmentation reduces overall complexity by breaking down the comprehensive analysis into manageable stages.
Data Source
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AI summary
The invention relates to a system for monitoring a person receiving non-invasive mechanical ventilation (NIV) using a medical ventilator supplying a breathing mask with a respiratory gas. The medical ventilator provides ventilation variables. A computer server receives these ventilation variables and processes them to determine, for each variable, several statistical indicators and derive a ventilation quality score using a mathematical model that compares these statistical indicators with stored thresholds obtained through machine learning. Finally, at least one significant ventilation variable is identified among these variables, impacting the ventilation quality score more than the other ventilation variables.