Medical Concept Search Engine with Bubble Graph Interface
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Solution Overview
Problem
Current search engines face difficulties in handling complex search criteria for medical information, limiting the number of criteria that can be utilized and lacking suggestions on how to refine searches based on predicted impact, resulting in users having to manually filter content and being unaware of the effects on search results.
Innovation Solution
A medical concept searching engine generates a concept index model that identifies the number of search results for given criteria, suggesting refinements and using a bubble graph interface to visually represent categories of medical concepts, allowing users to select and add related concepts to refine search results based on predicted impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If search engines limit the number of criteria that can be utilized, then the system complexity is reduced and ease of operation is improved, but the adaptability and completeness of search functionality deteriorates
Solution Approach 1:
The patent segments the search refinement process into distinct categories (e.g., medical conditions, treatments, symptoms) presented as separate bubbles. Each category can be independently selected, allowing users to navigate complex search criteria through organized, manageable segments rather than overwhelming them with a monolithic set of options.
Solution Approach 2:
The patent introduces a visual dimensional representation (bubble graph interface) that transforms the abstract concept of search criteria into a spatial layout. Bubbles are positioned and sized according to their relevance and impact, adding visual dimensions (size, position, color) to the search refinement process, making it more intuitive and manageable.
2Productivity
If search engines do not provide suggestions on how to refine searches, then the device complexity is reduced, but the productivity and effectiveness of the search process deteriorates
Solution Approach 1:
The patent implements feedback by displaying to users the predicted impact of selecting or deselecting specific search criteria. The system analyzes how each potential refinement would affect search results and communicates this information back to the user through the bubble size and visual indicators, enabling informed decision-making about search refinement.
Solution Approach 2:
The patent performs preliminary analysis of search criteria impacts before the user makes selections. The system pre-calculates and displays the potential effects of various refinement options, allowing users to make informed decisions without having to manually test each option. This preliminary action saves time and improves search productivity.
3Measurement precision
If users manually filter content without knowing the effects on search results, then the ease of operation is maintained, but the measurement precision of search outcome prediction deteriorates
Solution Approach 1:
The patent uses color coding in the bubble graph interface to convey information about search criteria impacts. Different colors or shading intensities indicate the level of impact or category type, providing immediate visual feedback to users about the consequences of their selections without requiring complex explanations or manual calculation.
Solution Approach 2:
The patent adds visual dimensions (bubble size, position, color) to represent abstract search impact metrics. The size of each bubble corresponds to the impact magnitude, while position and color encode additional information about category and relationship strength, transforming invisible prediction metrics into perceivable visual properties.
Data Source
AI summary
A mechanism is provided in a data processing system to implement a medical concept searching engine for improving searches of medical concepts based on an index model. The mechanism generates a concept index model data structure that records medical concepts and corresponding numbers of instances of the medical concepts in the corpus of documents. Responsive to receiving a search request from a user, the medical concept searching engine identifies at least one medical concept in the search request and one or more related medical concepts that are related to the at least one medical concept based on an ontology data structure. The medical concept searching engine generates a bubble graph user interface comprising a plurality of bubbles corresponding to the at least one medical concept and the one or more related medical concepts.