Neural Network Diagnosis of Pulmonary Hypertension from ECG Time Series
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Solution Overview
Problem
Current diagnostic techniques for health conditions, especially at an early stage, are challenging due to the cost, time-intensity, risk, and burden of existing methods, and they often rely on discrete metrics derived from time series data rather than the raw data itself.
Innovation Solution
The development of systems and methods that utilize patient time series data, such as ECG waveforms and spectrograms, to diagnose and classify patients through the use of neural network models trained on structured, unstructured, and semi-structured health data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic tests are used, then diagnostic accuracy can be achieved, but the process becomes costly, time-intensive, and burdensome
Solution Approach 1:
The patent extracts and analyzes specific time-series features (ECG waveforms, vital signs, lab results) from comprehensive patient data to identify patterns indicative of health conditions. This selective extraction of relevant temporal patterns enables accurate diagnosis without requiring full traditional diagnostic workups, thereby reducing time and resource consumption while maintaining diagnostic precision.
Solution Approach 2:
The system performs preliminary analysis of patient time-series data to identify early indicators of health conditions before symptoms become overt. By conducting preliminary screening using machine learning models on historical data, the system can flag high-risk patients for further evaluation, reducing the need for extensive diagnostic testing in low-risk cases and accelerating diagnosis in high-risk cases.
2Reliability
If traditional diagnostic tests are used, then health conditions can be diagnosed, but the process becomes costly and burdensome
Solution Approach 1:
The patent implements a universal diagnostic system that processes multiple types of patient data (ECG, vital signs, lab results, clinical notes) through a single machine learning framework. This multi-functional approach consolidates various diagnostic functions into one system, reducing overall complexity while maintaining reliable diagnosis across different health conditions through standardized time-series analysis methods.
Solution Approach 2:
The system creates simplified digital representations (models) of complex diagnostic processes by training machine learning algorithms on historical patient data. These digital copies capture the essential patterns of disease progression and can reliably diagnose conditions without requiring the full complexity of traditional diagnostic workflows, thereby reducing system complexity while preserving diagnostic reliability.
3Ease of operation
If discrete metrics are used for diagnosis, then analysis is simplified, but patterns in time series data that indicate early health conditions are lost
Solution Approach 1:
The patent transforms one-dimensional discrete metrics into multi-dimensional time-series representations, adding the temporal dimension to the analysis. By analyzing data points across multiple time points rather than single snapshots, the system captures evolving patterns and trends that discrete metrics miss, while machine learning algorithms automatically process this increased dimensionality to maintain analysis simplicity.
Solution Approach 2:
The system transitions from static discrete metrics to dynamic time-series analysis that captures how patient parameters change over time. This dynamic approach detects patterns such as rate of change, variability, and temporal relationships between different parameters, providing richer diagnostic information while automated processing maintains ease of operation through standardized analytical frameworks.
Data Source
AI summary
Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.