Probabilistic Event Classification Using Semantic Analysis
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Solution Overview
Problem
Current methods for classifying safety events in healthcare settings are inefficient, as they often rely on broad Event Types that do not provide sufficient cues for users to accurately select Nature and Sub-Nature classifications, leading to difficulties in determining the appropriate classification for a given event.
Innovation Solution
A probabilistic event classification system that uses textual content analysis to identify key terms, generate semantic representations, and search a safety event database to suggest potential event types based on statistical and semantic analysis, providing users with inferred classifications through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If broad Event Types are used for classification, then the classification system is simpler to operate, but the accuracy and precision of classification is reduced
Solution Approach 1:
The patent introduces an intermediary NLP processing layer between the raw event data and the classification system. This intermediary layer extracts semantic concepts and generates representations that serve as bridges, enabling the system to achieve both ease of operation through automated processing and high precision through semantic analysis. The intermediary processing transforms raw text into structured semantic representations that can be accurately mapped to classification categories.
Solution Approach 2:
The patent changes the parameter representation from simple broad categories to multi-level hierarchical classifications (Event Type, Nature, Sub-Nature). This parameter transformation allows the system to maintain operational simplicity at the high level while achieving fine-grained precision at lower levels. The hierarchical structure enables the system to operate easily at the top level while providing detailed accurate classification at deeper levels.
2Measurement precision
If manual classification methods are used, then classification accuracy can be maintained, but the time consumption and productivity is reduced
Solution Approach 1:
The patent implements self-service through automated NLP processing that performs classification without requiring manual intervention. The system automatically extracts semantic concepts, generates representations, and maps them to classifications. This self-service mechanism maintains high accuracy through sophisticated semantic analysis while dramatically improving productivity by eliminating manual processing time.
Solution Approach 2:
The patent replaces the mechanical manual classification process with an automated computational system. Instead of human operators manually reviewing and classifying events, the system uses NLP algorithms, semantic concept extraction, and machine learning models to automatically perform classification. This substitution maintains accuracy through intelligent processing while achieving high-speed automated classification.
3Measurement precision
If detailed Nature and Sub-Nature classifications are provided, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments the classification system into distinct hierarchical levels: Event Type, Nature, and Sub-Nature. This segmentation allows the system to manage complexity by dividing the classification task into manageable components. Each level handles specific aspects of classification, making the overall system more manageable while providing detailed precision at each level. The modular hierarchical structure reduces perceived complexity while maintaining detailed classification capability.
Solution Approach 2:
The patent performs preliminary action by pre-processing the event narratives to extract semantic concepts and generate representations before the actual classification occurs. This preliminary processing simplifies the main classification task by preparing the data in advance, reducing the complexity of the classification process while enabling detailed classifications. The pre-extraction of semantic information makes the subsequent classification more straightforward and manageable.
Data Source
AI summary
Probabilistic event classifications systems and method are provided herein. In one embodiment, a method includes receiving an event narrative, the event narrative comprising textual content describing a safety event, parsing the textual content to identify key terms, searching a safety event database for classifications associated with the key terms, selecting a set of classifications based on the key terms using statistical analysis, the set of classifications comprising potential event types for the event narrative, and displaying the set of classifications for the event narrative via a graphical user interface.