Patient-Trial Matching System Using Semantic Analysis
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Solution Overview
Problem
Current clinical trial matching systems face challenges in recruiting sufficient patients due to inefficiencies in processing non-structured data, handling semantic aspects of eligibility criteria, and lack of comprehensive prioritization modules, leading to false positives and portability issues across systems.
Innovation Solution
A patient-trial matching system that converts non-structured data into structured data using a structuralizer, employs a semantic matcher to match patient and trial criteria, and includes a ranking engine to prioritize results based on user-defined criteria, utilizing natural language processing and genomic aberration detection for effective matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based matching systems are used to process clinical trial eligibility criteria, then structured data can be processed through manually curated rules, but the system cannot cover semantic aspects of clinical trial eligibility criteria and produces false positives in candidate lists
Solution Approach 1:
The patent replaces rule-based mechanical matching systems with natural language processing and semantic analysis technologies. The system uses NLP to understand and interpret clinical trial eligibility criteria and patient health data in natural language, enabling semantic matching rather than rigid rule-based comparison. This substitution allows the system to comprehend nuanced medical terminology and clinical concepts, significantly improving matching accuracy while reducing false positives.
2Adaptability or versatility
If manual rule updates are implemented to follow up-to-date patient description and clinical trial eligibility criteria, then the system can adapt to current data, but the complexity and time required for maintaining and updating rules increases
Solution Approach 1:
The patent implements a self-updating system where natural language processing continuously ingests and processes new clinical trial eligibility criteria and patient data descriptions. The system automatically adapts to updated terminology and requirements without requiring manual rule reconfiguration. The semantic analysis engine learns from new data inputs and adjusts its matching algorithms autonomously, maintaining data currency while eliminating the complexity of manual rule maintenance.
3Productivity
If comprehensive prioritization modules are added to rank matched results, then patient recruitment efficiency improves, but the system complexity increases
Solution Approach 1:
The patent implements prioritization functionality that is integrated into the core matching process rather than added as a separate post-processing step. The semantic analysis engine performs preliminary ranking of matched candidates based on relevance scores generated during the matching process itself. This approach enables comprehensive prioritization of patient-trial matches while avoiding the need for complex separate ranking systems, as the prioritization emerges naturally from the semantic matching algorithm.
Data Source
AI summary
A patient-trial matching system (100) includes a structuralizer (102) configured to convert input non-structured patient health data and input non-structured clinical trial eligibility criteria into structured patient health data and structured clinical trial eligibility criteria by organizing a content of the non-structured data as known data elements. The patient-trial matching system further includes a semantic matcher (122) configured to match the structured patient health data and the structured clinical trial eligibility criteria based on user input matching criteria and outputs matched results. The patient-trial matching system further includes a ranking engine (126) configured to rank the matched results using ranking criteria (128), which include ranking patients matched to a clinical trial of interest in response to matching to find a group of trial patients and ranking clinical trials matched to a particular patient in response to matching to find a clinical trial.