Autoregressive Neural Networks for Noisy Vital Alert Filtering
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Solution Overview
Problem
Supervised machine learning approaches are resource-intensive and impractical for analyzing noisy electronic patient data in the medical industry, particularly due to the need for labeled data and high computational resources, which is not feasible for all stakeholders seeking to extract insights from electronic medical records (EMR) data.
Innovation Solution
An unsupervised machine learning model using autoregressive recurrent neural networks that leverages patient embeddings to predict the likelihood of a clinician dismissing alerts, improving the signal-to-noise ratio by identifying dependencies in patient vital data and reducing the number of reported alerts, thereby decreasing computational resources needed for monitoring and prioritizing actionable alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning approaches are used to analyze patient data, then measurement precision and reliability can be improved, but device complexity and resource consumption increase significantly
Solution Approach 1:
The system uses unsupervised learning where the model automatically learns patterns from raw patient data without requiring manual labeling or annotation. The autoregressive recurrent neural network self-trains on the data structure itself, eliminating the need for external supervision resources while maintaining analysis accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual data labeling and supervised training with an automated unsupervised learning mechanism. The system substitutes human-in-the-loop annotation processes with algorithmic pattern recognition that automatically identifies actionable alerts from raw vitals data.
2Ease of operation
If static thresholds are used for vital alerts, then ease of operation is improved, but measurement precision deteriorates due to low signal-to-noise ratio
Solution Approach 1:
The system transitions from static fixed thresholds to dynamic adaptive thresholds that adjust based on learned patterns in patient data. The autoregressive model continuously adapts threshold levels based on historical data patterns, patient-specific baselines, and contextual factors, maintaining operational simplicity while dramatically improving signal-to-noise ratio.
Solution Approach 2:
The patent changes the parameters used for alert determination from fixed numerical thresholds to dynamic probability scores generated by the neural network. The system transforms rigid threshold parameters into flexible, context-aware decision parameters that adapt to individual patient patterns and clinical contexts.
3Measurement precision
If supervised machine learning is implemented with labeled EMR data, then measurement precision improves, but loss of time and resource consumption increase due to manual labeling requirements
Solution Approach 1:
The unsupervised learning system performs self-training by automatically learning from the inherent structure of raw EMR data without requiring external labeling efforts. The model identifies patterns and relationships autonomously, eliminating the time-consuming manual annotation process while maintaining analytical precision.
Solution Approach 2:
The system performs preliminary pattern recognition and feature extraction automatically during the unsupervised learning process, preparing the data structure in advance for accurate alert identification. This preliminary automated processing replaces the need for subsequent manual labeling and data preparation steps.
Data Source
AI summary
An emerging service in the medical industry is to provide high quality remote care to patients by remotely monitoring patient vital information. In some instances, patient vitals information is collected at a much higher frequency in comparison to a traditional clinical environment. This frequent influx of patient health information can result in a considerable amount of health-related noise that health computing systems and clinicians must evaluate. The instant systems and methods leverage autoregressive recurrent neural networks and patient embeddings to predict the likelihood of needing to address certain patient information, thereby reducing the amount of health-related noise and to enable health computing systems and clinicians to use their resources more effectively.