DeepSOFA Model for ICU Acuity Scoring
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Solution Overview
Problem
Existing tools for assessing ICU patient acuity, such as the Sequential Organ Failure Assessment (SOFA) score, face challenges due to fixed cutoff points and sparse data in electronic health records, which hinders accurate and timely illness severity assessments.
Innovation Solution
A deep learning model, referred to as the DeepSOFA model, is employed to generate acuity scores and mortality predictions in real-time. This model uses a modified recurrent neural network with gated recurrent units (GRUs) and a self-attention mechanism, processing both static patient information and time-series biometric measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Sequential Organ Failure Assessment (SOFA) score is used to assess ICU patient acuity, then a standardized framework for evaluating organ system function is provided, but accuracy is hindered by fixed cutoff points and sparse data in electronic health records
Solution Approach 1:
The patent transforms the fixed cutoff point approach of traditional SOFA scoring into a continuous probability estimation framework using machine learning models. The system changes the parameter representation from discrete threshold-based scores to continuous probability values that adapt to the actual data distribution in electronic health records, thereby improving accuracy while handling sparse data effectively
Solution Approach 2:
The patent creates a virtual copy of the SOFA assessment framework enhanced with machine learning capabilities. Instead of directly using the traditional SOFA method with its limitations, the system implements a learned version that replicates the clinical reasoning process while overcoming data sparsity through patterns learned from historical patient data
2Ease of operation
If traditional SOFA scoring methods are used, then a structured approach to illness severity assessment is provided, but real-time determination is hindered by complexity
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive historical patient data before deployment. The models learn complex patterns and relationships in advance, so that during actual clinical use, acuity assessment can be performed rapidly by applying the pre-learned knowledge to new patient data without requiring complex real-time calculations
Solution Approach 2:
The patent replaces the manual mechanical process of calculating SOFA scores with automated machine learning models. Instead of clinicians or staff manually extracting data and computing scores according to fixed rules, the system uses trained algorithms that automatically process electronic health record data and generate acuity assessments, dramatically reducing time and operational complexity
3Reliability
If fixed cutoff points are used in SOFA scoring, then a standardized threshold-based assessment is provided, but accuracy is hindered by inability to capture continuous病情变化
Solution Approach 1:
The patent introduces dynamics by replacing static fixed cutoff points with dynamic, adaptive thresholds learned from data. The machine learning models continuously adapt to capture the evolving nature of patient condition, allowing the assessment to respond to subtle changes in organ function that fixed thresholds would miss, thereby improving both reliability and precision simultaneously
Data Source
AI summary
Methods, apparatus, systems, and computer program products for providing patient predictions are provided in various embodiments. Responsive to receiving an indication of initiation of a patient interaction, a model for the patient is initiated by an assessment computing entity. The model has been trained using machine learning and the model is configured to generate a prediction for the patient. The prediction comprises at least one of an acuity score or a mortality prediction. Responsive to identifying a prediction trigger, the assessment computing entity updates the model for the patient based at least in part on medical data corresponding to the patient. The assessment computing entity generates the prediction using the updated deep learning model. The assessment computing entity provides at least a portion of the prediction such that the at least a portion of the prediction may be used to update an electronic health record corresponding to the patient and/or provided to a clinician for review.