Knowledge-Grounded Clinical Trial Eligibility Criteria Generation
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Solution Overview
Problem
Current machine learning techniques are inadequate for generating eligibility criteria for clinical trials from protocol titles, as they lack richness, control, and interpretability, often relying solely on title information and struggling with the complexity and variability of eligibility criteria.
Innovation Solution
A knowledge-grounded model flow that enhances protocol titles with external knowledge, allowing for explicit control and transparency in generating eligibility criteria, using category and entity models to infer relevant criteria and enabling iterative refinement by clinical trial designers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard sequence-to-sequence learning is used to generate eligibility criteria from protocol titles, then the generation process is simplified, but the richness and quality of generated criteria deteriorates
Solution Approach 1:
The patent applies preliminary action by enhancing the protocol title with external knowledge (entities and categories) before generating eligibility criteria. This preprocessing step enriches the input information, allowing the generation model to produce higher quality criteria without increasing the fundamental complexity of the seq2seq architecture.
Solution Approach 2:
The patent introduces an intermediary enhancement module that bridges the protocol title and the eligibility criteria generation. This module enriches the title with external knowledge (entities and categories) before passing it to the seq2seq model, thereby improving criteria quality without fundamentally changing the generation process.
2Ease of operation
If eligibility criteria are generated solely from protocol titles, then the process is straightforward, but the criteria lack richness and diversity
Solution Approach 1:
The patent applies universality by using a multi-functional approach where the system not only generates eligibility criteria but also enriches protocol titles with external knowledge (entities and categories). This allows the same system to serve multiple purposes: title enhancement, knowledge extraction, and criteria generation, thereby improving criteria diversity while maintaining process simplicity.
Solution Approach 2:
The patent enriches protocol titles with external knowledge (entities and categories) before generating eligibility criteria. This preliminary enhancement action provides the generation model with diverse and rich input information, enabling it to produce more varied and adaptable criteria without complicating the overall process.
3Reliability
If transformer-based models are used for eligibility criteria generation, then model capacity is increased, but the maximum sequence length limitation is reached
Solution Approach 1:
The patent extracts only the essential elements (entities and categories) from external knowledge sources and integrates them into the protocol title enhancement. This extraction approach provides rich information to the model without including the full text of external knowledge sources, thereby maintaining model capacity while avoiding sequence length limitations.
Solution Approach 2:
The patent introduces an intermediary enhancement module that selectively extracts and integrates external knowledge (entities and categories) into the protocol title representation. This intermediary approach provides the transformer model with enriched information without exceeding its sequence length constraints.
4Device complexity
If black-box generation models are used, then model simplicity is maintained, but interpretability and control of generated content deteriorates
Solution Approach 1:
The patent introduces an intermediary enhancement module that explicitly processes and enriches the protocol title with external knowledge before generation. This intermediary step makes the generation process more transparent and controllable, as the enhancement logic is separate from the black-box seq2seq model, allowing for better interpretability without increasing model structural complexity.
Solution Approach 2:
The patent performs preliminary enhancement of protocol titles with external knowledge (entities and categories) before passing them to the generation model. This preliminary action makes the generation process more interpretable and controllable, as the enhancement step is distinct and can be independently analyzed, without complicating the overall model structure.
Data Source
AI summary
Disclosed herein is a model flow that generates eligibility criteria for a clinical trial based on eligibility criteria associated with a protocol title of the trial. Unlike standard black-box generation models, the techniques disclosed herein leverage existing knowledge to enhance the title. The enhanced title also acts as an intermediate between the title and the generated criteria clauses, enabling explicit control of the generated content as well as an explanation of why the generated content is relevant. The resulting workflow is knowledge-grounded, controllable, transparent, and interpretable.