Personalized Hypothetical Span Identification in Medical Text
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for natural language processing in medical texts struggle to accurately distinguish between actual factual statements and hypothetical descriptions, which can lead to erroneous diagnoses and treatments, as they often fail to differentiate between actual events and planned or potential actions.
Innovation Solution
A method involving the use of parse tree data structures and personalized dictionary data structures to identify and annotate hypothetical spans within medical texts, allowing for the differentiation between factual and hypothetical content, thereby enabling more accurate treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current natural language processing systems are used to analyze medical texts, then the processing speed and coverage are maintained, but the accuracy of distinguishing factual statements from hypothetical descriptions deteriorates
Solution Approach 1:
The patent segments the natural language text into individual spans and analyzes each span separately using a parse tree data structure. This segmentation allows the system to identify hypothetical markers (such as 'would', 'could', 'might') in specific contexts without being overwhelmed by the complexity of the entire text, thereby improving accuracy while managing system complexity through modular processing.
Solution Approach 2:
The patent introduces an intermediary layer consisting of a personalized dictionary data structure and confidence score calculation mechanism. This intermediary processes the relationship between hypothetical markers and their contextual meanings, allowing the system to accurately distinguish factual from hypothetical content without requiring complete reengineering of the entire NLP pipeline.
2Reliability
If hypothetical spans are identified and weighted differently, then the accuracy of treatment recommendations is improved, but the complexity of text processing increases
Solution Approach 1:
The patent applies local quality by assigning different weights to different portions of the text based on their hypothetical nature. Factual spans receive higher weights while hypothetical spans receive lower weights, allowing the treatment recommendation system to focus on reliable information without being unduly influenced by speculative content, thereby improving reliability with targeted rather than universal processing complexity.
Solution Approach 2:
The patent changes the parameter of text span weighting based on the identification of hypothetical markers. By dynamically adjusting the weight parameter for each text span according to its hypothetical confidence score, the system improves the reliability of treatment recommendations while managing complexity through parameter-based control rather than structural complexity.
3Measurement precision
If a personalized hypothetical dictionary is created for each source, then the precision of hypothetical span identification is improved, but the time and resources required for processing increase
Solution Approach 1:
The patent performs preliminary action by creating personalized hypothetical dictionary data structures for each text source in advance, before the actual treatment recommendation process. This allows the system to pre-identify and store hypothetical markers and their contextual patterns for each source, enabling faster and more precise hypothetical span identification during actual processing without repeating the analysis work.
Solution Approach 2:
The patent uses copying by creating personalized dictionary copies tailored to each text source's specific language patterns and hypothetical marker usage. Rather than analyzing each text from scratch, the system copies and adapts general hypothetical identification rules into source-specific dictionaries, improving precision while reducing the computational burden of repeated analysis.
Data Source
AI summary
Mechanisms receive natural language content and analyze the natural language content to generate a parse tree data structure. The mechanisms process the parse tree data structure to identify one or more instances of candidate hypothetical spans in the natural language content. Hypothetical spans are terms or phrases indicative of a hypothetical statement. The mechanisms calculate, for each candidate hypothetical span, a confidence score value indicative of a confidence that the candidate hypothetical span is an actual hypothetical span based on a personalized hypothetical dictionary data structure associated with a source of the natural language content. The mechanisms perform an operation based on the natural language content. The operation is performed with portions of the natural language content corresponding to the one or more identified instances of actual hypothetical spans being given different relative weights within portions of the natural language content than other portions of the natural language content.