Multimodal Cardiac Prediction via Unified Deep Learning Architecture
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Solution Overview
Problem
Conventional techniques for predicting cardiac conditions like atrial fibrillation are inefficient due to error propagation from separate machine learning models for ECG and EHR data, requiring significant overhead and unable to effectively combine different data types, leading to suboptimal modeling results and limitations in predicting disease onset.
Innovation Solution
A single architecture deep learning model is configured to process multiple dissimilar input feature modalities by receiving predefined configuration parameters, preprocessing, and processing these modalities through isolated layers to generate normalized outputs, which are then combined using a single loss function, enabling the mapping of complex non-linear relationships between ECG and EHR features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate machine learning models are trained for different data types (ECG, EHR), then each model can be specialized for its specific data type, but error propagation occurs and significant computational overhead is required for maintenance and deployment
Solution Approach 1:
The patent combines multiple separate machine learning models into a single unified neural network architecture that processes ECG, EHR, and other clinical data types simultaneously. This integration eliminates error propagation between separate models and reduces computational overhead while maintaining specialized processing capabilities through distinct neural network pathways for each data type.
Solution Approach 2:
The unified neural network model serves multiple functions by processing different data types (ECG waveforms, structured EHR data, imaging data) through a single architecture. The model can perform various predictive tasks including disease onset prediction, risk stratification, and diagnostic assistance, replacing the need for multiple specialized models.
2Ease of manufacture
If separate models are used for different data types, then each model can be independently trained, but the models cannot effectively map non-linear interactions between different feature types
Solution Approach 1:
The patent merges multiple data processing pathways into a unified neural network where ECG, EHR, and other clinical features are processed simultaneously. This allows the model to capture complex non-linear interactions between different feature types that would be impossible to model with separate independent models, while maintaining ease of training through a single unified training process.
3Device complexity
If traditional single architecture models are used, then model structure is simple, but they cannot effectively process dissimilar input feature modalities like ECG, imaging, and structured data
Solution Approach 1:
The patent segments the unified neural network into distinct processing pathways or modules, each optimized for specific data types (ECG processing module, EHR processing module, imaging processing module). These segmented modules work together within a single architecture, allowing the model to process dissimilar input modalities effectively while maintaining relative structural simplicity through modular design.
Solution Approach 2:
The patent applies local quality by giving different parts of the neural network architecture specialized properties suited for their specific data types. For example, convolutional layers are used for ECG waveforms, fully connected layers for structured EHR data, and appropriate architectures for imaging data, while all components integrate into a unified model with shared loss functions and training procedures.
Data Source
AI summary
A method for configuring a single architecture deep learning model includes receiving parameters; configuring segments; and processing segment, combination and/or fully-connected outputs to generate modeling output. A computing system comprising: a processor; and a memory having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive parameters; configure segments; and process segment, combination and/or fully-connected outputs to generate modeling output. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to: receive parameters; configure segments; and process segment, combination and/or fully-connected outputs to generate modeling output.