AI Knowledge Graph Construction for Medical Information
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Solution Overview
Problem
The rapid growth of medical knowledge makes it challenging for both the general public and medical professionals to access the latest, trustworthy information, as existing search technologies are inefficient and prone to misinformation, especially during crises like pandemics.
Innovation Solution
The development of online, interactive knowledge graphs using artificial intelligence models to automatically construct and fuse peer-reviewed medical data into searchable graphs, enabling efficient access to up-to-date, trustworthy information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search engines are used to access medical information, then users can search for medical topics, but the information is unstructured, time-consuming to navigate, and prone to misinformation
Solution Approach 1:
The patent segments medical information into structured knowledge graphs with defined entities, relationships, and attributes. Medical literature is parsed into discrete factual statements that are organized into hierarchical knowledge structures, enabling precise retrieval and verification of individual medical facts without navigating through entire documents or unstructured search results.
Solution Approach 2:
The patent introduces an intermediary layer of structured knowledge graphs between raw medical literature and users. This intermediary automatically extracts, validates, and organizes medical information from peer-reviewed sources into a standardized format, serving as a trusted mediator that filters out misinformation and presents only verified medical facts in an easily accessible format.
2Loss of information
If medical knowledge is rapidly published to stay current, then the latest findings are available, but the volume of information increases making it difficult to access and verify
Solution Approach 1:
The patent implements dynamic knowledge graphs that automatically update as new peer-reviewed medical literature is published. The system continuously ingests new studies, extracts factual information, and integrates it into the existing knowledge structure, ensuring the medical knowledge base remains current without requiring manual updates or reorganization of existing content.
Solution Approach 2:
The patent changes the parameter of information organization from unstructured text to structured factual statements with defined schemas. Each medical fact is represented with standardized parameters (entities, relationships, confidence scores, source citations), transforming the complexity of raw medical literature into a manageable, queryable format that scales with increasing volume of published research.
3Loss of information
If comprehensive medical literature is collected to ensure all findings are included, then the knowledge base is complete, but searching and reading hundreds of pages is prohibitively slow
Solution Approach 1:
The patent extracts only the essential factual information from comprehensive medical literature, separating key medical facts from the surrounding text, methodology, and discussion. This extraction process pulls out critical findings, conclusions, and data points while leaving behind the voluminous original text, enabling users to access complete medical knowledge through concise, structured facts rather than reading entire publications.
Solution Approach 2:
The patent creates simplified copies of medical knowledge in the form of structured factual statements that capture the essential information from original publications. These copies retain the core medical findings and are linked back to source literature, providing a fast-access representation that preserves completeness while dramatically reducing the time required to access and understand medical information.
Data Source
AI summary
Described herein are systems and methods providing online, interactive, trustworthy knowledge graphs for a specific topic (e.g., COVID-19) and search engines using such knowledge graphs. In some aspects, a method for automatically constructing knowledge graphs includes: accessing a dataset, the dataset including a plurality of articles related to a specific topic; classifying, using a first artificial intelligence (AI) model, a plurality of tables within the dataset; classifying, using a second AI model, a plurality of hierarchal metadata of the tables; and fusing, using a third AI model, the hierarchal metadata into a knowledge graph, the knowledge graph being associated with the specific topic.