Computational Medical Evidence Modeling for Reliable AI Reasoning
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Solution Overview
Problem
Current medical evidence is predominantly narrative and document-based, leading to inefficiencies such as delayed diffusion, ineffective scalability, alert fatigue, and minimal feedback, which are exacerbated by the inability of existing AI architectures to represent and process this knowledge controllably or reliably, and the limitations of standard ontologies in enabling controllable use by AI systems.
Innovation Solution
A computational evidence platform that converts medical evidence into structured, computational elements, allowing for real-time querying and integration with patient data, and enabling inferential reasoning across healthcare stakeholders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If medical evidence is represented as narrative documents and guidelines, then human interpretation and expert assembly are enabled, but diffusion speed, scalability, and feedback efficiency are severely limited
Solution Approach 1:
The patent transforms medical evidence from narrative text format to structured computational data formats (JSON, XML, RDF). This parameter change enables machine processing, automated reasoning, and efficient diffusion across systems while maintaining the underlying medical knowledge integrity.
Solution Approach 2:
The patent replaces manual human interpretation and assembly of medical evidence with automated computational systems. AI models automatically extract, validate, and integrate evidence from multiple sources, eliminating the bottleneck of human-centric knowledge assembly and enabling rapid diffusion.
2Reliability
If AI architectures based on text alone are used, then processing of narrative evidence is possible, but controllable and reliable representation and reasoning is not achieved
Solution Approach 1:
The patent segments medical knowledge into discrete, structured components (concepts, relationships, evidence statements) that can be independently processed and reasoned about. This segmentation enables reliable AI reasoning through modular computational operations rather than monolithic text processing.
Solution Approach 2:
The patent introduces structured data formats and ontologies as intermediaries between narrative medical evidence and AI processing. These intermediaries translate unstructured text into standardized computational representations that enable reliable reasoning while managing architectural complexity.
3Adaptability or versatility
If standard ontologies are mapped to documents, then meta-data provision is enabled, but controllable use by AI systems is not achieved
Solution Approach 1:
The patent creates dynamic, flexible knowledge representations that can adapt to different AI system requirements while maintaining standardized ontological foundations. The system allows configurable mapping between standard ontologies and domain-specific concepts, enabling controllable AI operations.
Solution Approach 2:
The patent builds a universal knowledge representation framework that serves multiple functions: standard ontology mapping, AI-friendly data structuring, and flexible querying capabilities. This multi-functional approach enables both versatility in knowledge integration and controllable AI operation.
4Productivity
If manual search and interpretation of medical evidence is performed, then accurate clinical judgment is possible, but time efficiency and scalability are severely reduced
Solution Approach 1:
The patent performs preliminary extraction, validation, and structuring of medical evidence automatically before it is needed for clinical decisions. This pre-processing eliminates the need for manual search during critical decision-making moments, significantly reducing time lag.
Solution Approach 2:
The patent enables continuous automated updating and diffusion of medical evidence as new research emerges, rather than periodic manual reviews. This continuous process ensures clinicians always have access to current evidence without time loss.
5Adaptability or versatility
If narrative medical evidence is converted to computational elements, then scalability and real-time processing are enabled, but system complexity increases
Solution Approach 1:
The patent implements nested data structures where computational evidence elements are organized in hierarchical containers (concepts, relationships, evidence statements, sources). This nesting enables scalable organization of vast amounts of data while maintaining manageable structural complexity through standardized patterns.
Data Source
AI summary
A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.