Machine Learning Valvular Heart Disease Outcomes Forecasting

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Solution Overview

Problem

Clinicians face challenges in accurately determining the timing of valvular intervention for patients with valvular heart disease due to the complex interplay of multiple risk factors, lacking robust evidence for decision-making, especially for aortic valve stenosis, which is prevalent and associated with increased mortality.

Innovation Solution

A computer-implemented system using machine learning techniques to predict patient outcomes and assess the severity of valvular heart disease, incorporating multi-modal data such as echocardiogram, computed tomography, and electronic medical records to determine the appropriateness and type of cardiac valve procedure, such as SAVR or TAVR, by training outcomes forecasting models and employing ensemble learning methods.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If clinicians rely on traditional assessment methods for valvular heart disease, then the decision-making process remains simple and quick, but the accuracy of predicting patient outcomes and determining intervention timing is insufficient

Engineering Contradiction:

Improveaccuracy of predicting patient outcomesVSAvoidcomplexity of assessment system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the complex prediction task into multiple specialized machine learning models, each trained on specific data modalities (echocardiogram, CT, ECG, electronic health records). These segmented models process different aspects of patient data independently and their results are integrated to produce comprehensive outcome predictions, thereby improving accuracy while managing complexity through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary machine learning system that acts as a mediator between raw multi-modal clinical data and clinical decision-making. This intermediary layer processes and synthesizes information from diverse sources (imaging, physiological signals, electronic records) to generate standardized outcome predictions, bridging the gap between complex data and actionable clinical insights

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If clinicians use comprehensive multi-modal data for assessment, then the accuracy of disease severity determination improves, but the time and resources required for data collection and analysis increase

Engineering Contradiction:

Improveaccuracy of disease severity determinationVSAvoidtime for data collection and analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary processing and standardization of multi-modal data during the data collection phase, preparing images, signals, and records in advance for rapid analysis. Pre-processing steps including image normalization, signal filtering, and data annotation are completed beforehand, reducing the time required for actual disease severity assessment while maintaining comprehensive data utilization

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual clinical assessment mechanisms with automated machine learning systems that process multi-modal data. Instead of clinicians manually reviewing echocardiograms, CT scans, and electronic records, the system uses automated image processing, signal analysis, and data integration algorithms to rapidly determine disease severity, significantly reducing analysis time while improving consistency and accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If traditional risk assessment methods are used, then the ease of operation is maintained, but the reliability of intervention timing decisions is insufficient

Engineering Contradiction:

Improvereliability of intervention timing decisionsVSAvoidease of clinical decision-making

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system incorporates feedback mechanisms where machine learning models continuously learn from outcomes of previous intervention decisions. By analyzing actual patient outcomes after valve replacement procedures, the system refines its predictions and recommendations, improving the reliability of intervention timing decisions over time while providing actionable feedback to clinicians in a user-friendly format

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240379239A1Automated detection and management of vavlular heart disease using machine learning

Publication Date: 2024.11.14 GE PRECISION HEALTHCARE LLC

AI summary

Techniques are described for computer-implemented techniques for managing various aspects of the cardiac care pathway using machine learning. According to an embodiment, a method can include training an outcomes forecasting model to predict patient outcomes resulting from undergoing a cardiac valve procedure using multi-modal training data for a plurality of different patients, wherein the training comprising separately training different machine learning sub-models of the forecasting model to predict preliminary patient outcome data and mapping the preliminary patient outcome data to the patient outcomes, resulting in a trained version of the outcome forecasting model. The method further includes applying the trained version of the outcomes forecasting model to new multi-modal data for a new patient to predict the patient outcomes for the new patient resulting from undergoing the cardiac value procedure.