Pulse Oximeter and Ventilator Data for Extubation Readiness Prediction
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Solution Overview
Problem
There is a lack of standardized and objective methods for assessing extubation readiness in patients, particularly preterm infants, leading to variable practices among institutions and increased morbidity and mortality due to untimely extubation or reintubation.
Innovation Solution
A system and method utilizing machine-learning analysis of pulse oximeter and ventilator data to classify lung disease state and predict extubation success or failure, generating predictive values for timely extubation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized objective assessment methods are implemented, then measurement precision and reliability improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The system integrates multiple existing medical devices (pulse oximeter, ventilator, EHR) into a unified assessment platform that serves both monitoring and prediction functions. The machine learning model processes diverse data types (physiological parameters, ventilator settings, laboratory values) through a single standardized algorithm, enabling multi-functional assessment without requiring separate specialized equipment for each measurement type.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between raw clinical data and extubation readiness assessment. This intermediary layer processes and synthesizes data from multiple sources, transforming complex multi-dimensional inputs into a standardized prediction output that clinicians can interpret, thereby reducing the complexity burden on the end-user while maintaining high measurement precision.
2Reliability
If machine learning models are deployed, then predictive accuracy improves, but ease of operation and accessibility deteriorate
Solution Approach 1:
The system is designed to automatically collect, process, and analyze data without requiring manual intervention from clinicians. The machine learning model continuously monitors patient parameters and generates extubation readiness predictions autonomously, eliminating the need for complex manual assessments while maintaining high reliability. Clinicians simply review the generated recommendations rather than operating complex analysis tools.
Solution Approach 2:
The system incorporates feedback loops where prediction outcomes are continuously evaluated against actual extubation results. This feedback mechanism allows the machine learning model to refine its predictions over time while providing clinicians with increasingly accurate and easier-to-interpret recommendations. The feedback system translates complex model outputs into actionable clinical insights that improve ease of operation.
3Measurement precision
If multiple data sources are integrated, then measurement precision improves, but loss of time and processing complexity increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction in advance, preparing data structures and algorithms before actual assessment is needed. By pre-processing data from multiple sources and establishing prediction models beforehand, the system minimizes real-time processing requirements while maintaining high measurement precision. Critical data transformations and model training occur during off-peak periods, reducing time loss during clinical decision-making.
Solution Approach 2:
The patent merges data collection, processing, and analysis functions into a single integrated workflow that occurs simultaneously rather than sequentially. Multiple data sources are combined in real-time through a unified processing pipeline, eliminating redundant operations and reducing overall processing time. The consolidation of these functions maintains comprehensive data integration while optimizing time efficiency for clinical use.
Data Source
AI summary
Embodiments provided herein include systems and methods for determining extubation readiness. One embodiment of a method includes obtaining pulse oximeter data and ventilator data for a patient that has been intubated over a predetermined monitoring period, classifying a lung disease state of the patient as acute or chronic based on at least one of the following: an age of the patient or a clinical indicator, and providing the pulse oximeter data, the ventilator data, and a lung disease classification to a trained predictive model previously trained on training data. Some embodiments include calculating a likelihood of extubation success or failure within a clinically relevant time window, and generating at least one of the following: a positive predictive value (PPV) or a negative predictive value (NPV) and generating and presenting, by the computing device, a readiness output based on the likelihood of extubation success or failure for determining extubation timing.