Machine Learning Clinical Trial Editing for Criteria Verification
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Solution Overview
Problem
Clinical trials often have complex documents with confusing, inaccurate, or contradictory patient selection criteria due to manual inspection and analysis inefficiencies, which can lead to missed issues and time-consuming corrections.
Innovation Solution
A machine learning-based method for clinical trial editing that identifies relevant documents, provides confidence values for criteria, prompts users to verify low-confidence criteria, generates alternative criteria for ambiguous ones, and suggests new criteria based on related trials, thereby automating the analysis and improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection and analysis of clinical trial criteria is performed, then accuracy of criterion verification can be achieved, but time consumption and inefficiency increase significantly
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated machine learning-based system. The ML model analyzes clinical trial documents, extracts criteria, and verifies their consistency and accuracy automatically, substituting human manual review with computational analysis that achieves both high accuracy and efficiency.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the clinical trial document and the final verification outcome. This intermediary automatically processes the document, identifies criteria, checks for contradictions and ambiguities, and presents findings to users, thereby reducing direct manual inspection time while maintaining reliability.
2Reliability
If comprehensive manual analysis of all criteria is performed to ensure accuracy, then completeness of issue detection improves, but productivity decreases
Solution Approach 1:
The patent applies partial action by having the machine learning model automatically analyze all criteria for basic consistency and completeness, then presenting only the identified issues and ambiguous areas for manual review. This approach ensures comprehensive issue detection while improving productivity by eliminating the need for manual review of clearly correct criteria.
Solution Approach 2:
The automated ML-based analysis system performs the comprehensive review that would otherwise require manual effort, achieving complete issue detection at machine speed. The system scans entire documents, identifies all criteria, checks for contradictions, and flags ambiguities automatically, vastly improving editing productivity.
3Reliability
If complex clinical trial documents with reused content are reviewed manually, then potential inconsistencies can be identified, but the complexity of the review process increases
Solution Approach 1:
The patent replaces complex manual review processes with automated machine learning analysis. The ML model handles document parsing, criterion extraction, consistency checking, and ambiguity detection automatically, reducing the perceived complexity for users while maintaining high reliability in inconsistency detection.
Solution Approach 2:
The system performs self-service by automatically analyzing the clinical trial document, identifying its own areas of inconsistency and ambiguity, and presenting findings to users. This self-analyzing capability reduces the need for complex manual review procedures while ensuring thorough consistency checking.
Data Source
AI summary
Methods and apparatuses for performing clinical trial editing using machine learning are provided. One example method generally includes receiving information of a first clinical trial that is being drafted and identifying, in a corpus of literature, a plurality of documents that are relevant to the first clinical trial, based on the title of the first clinical trial. The method further includes providing the plurality of documents and the plurality of criteria to a machine learning model configured to output for each respective criterion of the plurality of criteria, a confidence value for the respective criterion, receiving as output from the machine learning model the confidence value for each respective criterion and, upon determining that a first criterion has a first confidence value below a predefined threshold, prompting a user to verify the first criterion.