ML Risk Prediction for Heart Failure Decompensation
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Solution Overview
Problem
Current methods for predicting heart failure decompensation in patients are inadequate, relying on static data snapshots, invasive devices, or subjective telemonitoring, which fail to accurately and continuously assess the risk of decompensation, leading to late intervention and high rehospitalization rates.
Innovation Solution
A system combining static electronic medical records with dynamic data from wearable cardiovascular physiology monitors using machine-learning models to generate continuous risk scores, allowing for real-time assessment and personalized intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static electronic medical records are used alone, then device complexity is reduced, but measurement precision of decompensation risk is insufficient
Solution Approach 1:
The patent combines static electronic medical records with dynamic wearable monitor data into a unified machine-learning model that processes both data types together to generate comprehensive risk scores, thereby improving measurement precision without requiring separate independent systems
Solution Approach 2:
The machine-learning model serves multiple functions by simultaneously processing static EMR data and dynamic wearable data, evaluating multiple risk factors, and generating continuous risk scores, thereby reducing the need for multiple separate monitoring systems
2Measurement precision
If wearable cardiovascular physiology monitors are used, then measurement precision of real-time risk assessment is improved, but loss of time for data processing increases
Solution Approach 1:
The machine-learning model is pre-trained on comprehensive datasets including both static EMR data and dynamic wearable data patterns, enabling it to rapidly process new incoming data without requiring extensive real-time computation or data aggregation delays
Solution Approach 2:
The system continuously processes wearable monitor data streams in real-time without interruption, generating continuous risk scores that update as new data arrives, thereby maintaining measurement precision while minimizing processing delays through uninterrupted data flow
3Reliability
If conventional cardiovascular monitoring equipment is used, then device complexity is reduced, but reliability of decompensation prediction is insufficient
Solution Approach 1:
The system transitions from static monitoring to dynamic continuous monitoring by integrating wearable devices that capture real-time physiological changes, allowing the risk assessment to adapt and update continuously as patient conditions change, thereby improving prediction reliability
Solution Approach 2:
The machine-learning model incorporates feedback loops where continuous wearable data is compared against predicted risk patterns, allowing the system to identify deviations early and generate alerts, thereby improving the reliability of decompensation predictions through continuous validation
Data Source
AI summary
A method for determining a risk of decompensated heart failure in a user includes receiving a first set of data that is fixed with respect to time. A machine-learning model generates one or more initial risk factors based on the first set of data. A second set of data for the user that dynamically updates over time is received from a wearable cardiovascular physiology monitor. The machine-learning model is used to generate dynamic data classifiers based on the one or more initial risk factors. Aggregate risk scores for the user are then indicated based on an evaluation of the second set of data against the dynamic data classifiers. In this way, static electronic medical records may be combined with dynamic, real-time data from wearable cardiovascular physiology monitors to provide an accurate and continuously updating risk of decompensated heart failure for a user.