Query Generation System for Patient Information Retrieval
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Solution Overview
Problem
Patients with chronic diseases like cancer often struggle to find relevant information due to emotional and psychological challenges, and lack of guidance, leading to overwhelming amounts of irrelevant data from various sources.
Innovation Solution
A system that extracts relevant terms from patient documents, associates them with semantically related categories, and generates personalized queries to retrieve tailored information, including a comparator to rank results by relevance and a complexity unit to adapt document complexity to the user's understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If patients search for information using general search engines, then they can access a wide range of information sources, but they are overwhelmed with irrelevant data and cannot find relevant information
Solution Approach 1:
The system extracts relevant terms from patient documents (such as discharge summaries) and uses these extracted terms to construct targeted search queries. This extraction process filters out irrelevant information from the beginning, ensuring that only information related to the patient's specific condition and treatment is retrieved, thereby resolving the contradiction between information quantity and relevance.
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between the patient's medical documents and search engines. This intermediary analyzes the documents, identifies key terms, and formulates precise search queries that mediate between the general search engine and the patient's specific information needs, thus improving information relevance without losing the broad access to information sources.
2Reliability
If comprehensive information is provided to patients, then patients receive complete educational material, but patients still seek more sources and are overwhelmed
Solution Approach 1:
The system enables patients to automatically generate their own personalized search queries based on their medical documents without requiring them to manually search multiple sources. The automated query generation and result ranking process serves the patient's information needs independently, reducing the complexity of information management while maintaining completeness and reliability of the information provided.
Solution Approach 2:
The system changes the parameters of information retrieval by dynamically adjusting search queries based on the specific content of patient documents. Instead of providing static comprehensive information, the system adapts the information retrieval process to each patient's unique condition, treatment, and document content, thereby maintaining information completeness while simplifying the management complexity through parameter-based adaptation.
3Loss of information
If patients manually search for information, then they can find relevant data, but they lack guidance and do not know what information to look for
Solution Approach 1:
The system performs preliminary analysis of patient documents before the patient begins searching for information. By pre-extracting relevant terms and pre-formulating search queries based on the medical documents, the system prepares the information retrieval process in advance, guiding patients to find relevant information without requiring them to manually determine what to search for, thus improving both relevance and ease of operation.
4Adaptability or versatility
If the system generates personalized queries, then information retrieval is tailored to patient needs, but the system complexity increases
Solution Approach 1:
The system segments the complex query generation process into distinct functional modules: document analysis, term extraction, query formulation, and result ranking. Each module handles a specific aspect of the personalization process independently, making the overall system more manageable and maintainable while achieving high adaptability and versatility in generating personalized queries for different patients and their unique information needs.
Data Source
AI summary
A system for generating a query includes a term unit (1) for extracting a term from at least one input document (51), to obtain an extracted term. A category unit (2) is arranged for associating the extracted term with a category that is semantically related with the extracted term. A query unit (3) is arranged for generating a query in dependence on the extracted term and the category. The query unit (3) includes an additional term unit (4) for generating at least one additional search term based on the category, and the query unit (3) is arranged for including the additional search term in the query. A submit unit (5) is arranged for submitting the query to at least one search engine (50), to obtain a plurality of found documents.