Universal Precursor Pattern Detection for Medical Events
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Solution Overview
Problem
Current eHealth systems require domain-specific knowledge to identify precursor patterns for medical events, and most algorithms need prior knowledge of pattern duration and timing relative to clinical episodes, making them inflexible for application across different medical conditions.
Innovation Solution
A computer-implemented method generates a precursor pattern detection model by extracting and filtering features from annotated monitoring data, allowing for the detection of medical events without domain-specific knowledge, using a system comprising patient monitoring, data processing, and pattern detection systems that preprocess, feature extract, and classify data to create models for various medical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific knowledge and prior knowledge of pattern duration and timing are used to develop precursor pattern discovery algorithms, then the algorithms can achieve accurate detection for specific medical conditions, but the algorithms cannot be applied to different medical conditions without significant modification
Solution Approach 1:
The patent creates a universal precursor pattern discovery algorithm that can be applied across multiple medical conditions without significant modification. The system uses general machine learning techniques (support vector machines, decision trees, neural networks) that do not require domain-specific knowledge or prior assumptions about pattern duration and timing. This allows the same algorithmic framework to detect precursor patterns for different medical conditions by simply training on condition-specific data.
Solution Approach 2:
The system adapts to different medical conditions by changing the training data parameters rather than modifying the algorithm structure. The machine learning models are trained on annotated monitoring data specific to each medical condition, allowing the same algorithm to achieve condition-specific accuracy through parameter adjustment (training data) rather than structural modification.
2Reliability
If traditional precursor pattern discovery algorithms are used that require prior knowledge of pattern duration and timing, then detection can be achieved for known patterns, but the algorithms become impossible to apply to different medical conditions without significant modification
Solution Approach 1:
The system enables the algorithm to automatically determine pattern duration and timing characteristics by training on annotated data, eliminating the need for manual configuration. The machine learning models self-adjust to the specific characteristics of each medical condition through the training process, performing the configuration task automatically without requiring domain expertise or manual setup.
Solution Approach 2:
The system performs preliminary training on annotated monitoring data before deployment, allowing the algorithm to learn the specific pattern characteristics of each medical condition in advance. This preliminary training action captures the condition-specific parameters automatically, so that when the algorithm is deployed, it already has the necessary knowledge without requiring complex configuration or domain-specific input.
3Ease of operation
If eHealth systems focus on remote monitoring and abnormality detection with data collection infrastructure, then low-cost and convenient monitoring is achieved, but the systems lack the capability to mine and analyze sensor data for precursor patterns
Solution Approach 1:
The patent introduces an intermediary data processing layer between the simple data collection infrastructure and the clinical application. This intermediary layer includes machine learning models trained to detect precursor patterns, which transform the raw monitoring data into actionable predictions. This intermediary enables the system to maintain its simplicity and low cost while adding sophisticated pattern recognition capabilities that prevent loss of precursor information.
Data Source
AI summary
Systems and methods for generalized precursor pattern discovery that work with a wide range of biomedical signals and applications to detect a wide range of medical events are disclosed. In some embodiments, the methods and systems do not require domain-specific knowledge or significant reconfiguration based on the medical event being analyzed, hence it is also possible to discover patterns previously unknown to experts. In some embodiments, to build precursor pattern detection models, the system obtains annotated monitoring data. Positive and negative segments are extracted from the annotated monitoring data, and are preprocessed. Features are extracted from the preprocessed segments, and selected features are chosen from the extracted features. The selected features are classified to create the precursor pattern detection model The precursor pattern detection model may then be used in real time to detect occurrences of the medical event of interest.