Adaptive Threshold Tuning for Patient Deterioration Prediction
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Solution Overview
Problem
Current methods for predicting patient deterioration in hospital settings, such as intensive care unit transfers and unforeseen events, suffer from high false alarm rates and lack of explanatory power, leading to clinician skepticism and inefficiency.
Innovation Solution
A computer-implemented method using machine learning, specifically reinforcement learning and deep learning models, to analyze digital health data, adaptively tune prediction thresholds based on clinician behavior, and generate alarms with explanations for patient risk scores, reducing false alarms and improving predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based scoring systems (e.g., MEWS, Rothman Index) or statistical learning systems (e.g., eCART) are used to predict patient deterioration, then early detection capability is improved, but false alarm rate increases and reliability decreases
Solution Approach 1:
The patent replaces traditional rule-based scoring systems and statistical learning models with a neural network-based machine learning system. This substitution enables the system to automatically learn complex patterns from electronic health record data without relying on pre-defined clinical rules, thereby reducing false alarms while maintaining early detection capability. The neural network processes multiple patient parameters simultaneously and adapts to individual patient baselines, improving both precision and reliability.
Solution Approach 2:
The system dynamically adjusts prediction thresholds and parameters based on individual patient characteristics and historical data. Instead of using fixed thresholds for all patients, the machine learning model adapts parameters such as risk score thresholds and alert sensitivities to each patient's baseline condition, thereby reducing false alarms for stable patients while maintaining high sensitivity for those at risk of deterioration.
2Reliability
If complex machine learning models are used to improve predictive accuracy, then prediction reliability is improved, but system complexity and difficulty of interpretation increase
Solution Approach 1:
The patent introduces an intermediary layer that translates complex neural network predictions into clinically interpretable risk scores and explanations. The system generates human-readable reports that highlight the most significant factors contributing to predicted deterioration, enabling clinicians to understand and trust the model's recommendations without needing to interpret the underlying complex mathematics or neural network architecture.
3Ease of manufacture
If traditional prediction systems are used, then implementation simplicity is maintained, but clinical utility and actionable insight are reduced
Solution Approach 1:
The system incorporates feedback mechanisms that provide clinicians with actionable insights and explanations for predictions. The machine learning model continuously learns from clinician responses and outcomes, adjusting its predictions and explanations to better match actual patient trajectories. This feedback loop enhances the system's clinical utility by providing increasingly accurate and actionable information while maintaining ease of use through automated processing.
Data Source
AI summary
A method includes receiving patient health data; determining a score using a trained machine learning model; determining a threshold value using an adaptive threshold tuning learning model; comparing the score to the threshold value; and generating an alarm. A computing system includes a processor; and a memory having stored thereon instructions that, when executed by the processor, cause the computing system to: receive patient health data; determine a score using a trained machine learning model; determine a threshold value using an adaptive threshold tuning learning model; compare the score to the threshold value; and generate an alarm. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive patient health data; determine a score using a trained machine learning model; determine a threshold value using an adaptive threshold tuning learning model; compare the score to the threshold value; and generate an alarm.