Real-Time Cardiovascular Outcome Prediction Using Dynamic Clinical Data
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Solution Overview
Problem
Current approaches fail to provide real-time forecasting of adverse cardiovascular outcomes in COVID-19 patients, particularly cardiac arrest and thromboembolic events, due to their reliance on static clinical data and inability to account for the dynamic nature of the disease.
Innovation Solution
The development and validation of the COVID-HEART predictor, a machine learning model that generates a continuously updating risk prediction system by processing dynamic and static clinical parameters using sliding time windows, selecting relevant features, and training classifiers to forecast cardiovascular outcomes in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static clinical data is used for prediction, then the model is simpler to implement, but it cannot capture the dynamic nature of the disease and provides no real-time forecasting
Solution Approach 1:
The patent transforms static clinical data models into dynamic prediction systems by implementing sliding time windows that continuously update risk predictions as new clinical data becomes available. The system processes time-series data from electronic health records, allowing the model to adapt to changing patient conditions and provide real-time forecasting of cardiovascular outcomes.
Solution Approach 2:
The prediction system operates continuously by processing incoming clinical data streams without interruption. The sliding time window approach ensures that risk assessments are continuously updated rather than performed at discrete intervals, maintaining an ongoing evaluation of patient risk status throughout their hospitalization.
2Reliability
If all clinical parameters are processed, then the prediction comprehensiveness is improved, but the computational burden and processing time increase
Solution Approach 1:
The system extracts and processes only the most relevant clinical parameters from the electronic health record data stream. By identifying and focusing on key predictors of cardiovascular outcomes, the model achieves comprehensive prediction coverage without being overwhelmed by the full complexity of all available clinical data.
Solution Approach 2:
The patent implements a balanced approach where the model processes a subset of clinically relevant parameters at each time step rather than all available data. This partial processing approach maintains prediction accuracy while reducing computational burden and enabling faster processing speeds for real-time applications.
3Adaptability or versatility
If real-time dynamic prediction is implemented, then the clinical utility is improved, but the data processing complexity and computational resources required increase
Solution Approach 1:
The system introduces intermediate processing layers that bridge raw clinical data and final predictions. These intermediate representations organize and structure incoming data streams, making them more manageable for the prediction algorithm while preserving the dynamic, real-time nature of the assessment.
Solution Approach 2:
The prediction system segments the data processing task into manageable components: data ingestion from EHR, time-window extraction, feature processing, risk calculation, and output generation. This segmentation allows each component to be optimized independently and facilitates real-time processing by breaking down the complex overall task into smaller, parallelizable operations.
Data Source
AI summary
Provided herein are methods of generating models for prognosing cardiovascular outcomes for monitored subjects infected with an etiologic agent (e.g., severe acute respiratory syndrome coronavirus-2 or another etiologic agent). Related methods, systems, and computer program products are also provided.