Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study
The reliance on unstructured free text for documenting clinical trial protocols creates a significant barrier to automated reasoning, cohort discovery, and trial simulation. The lack of formal structure obscures critical temporal phenotypes, such as dynamic eligibility criteria and event timing constraints. Although Temporal Ensemble Logic (TEL) offers an expressive framework for modeling these elements, manual encoding remains a prohibitive bottleneck. We introduce the CT-TEL workflow: a scalable pipeline leveraging Large Language Models (LLMs) to translate narrative clinical protocols into TEL formulas. We applied CT-TEL to generate logical models for 23 real-world trials from ClinicalTrials.gov. We evaluated translation fidelity via a back-translation approach, using LLMs to convert TEL formulas back into natural language and measuring semantic similarity against source texts. The resulting semantic retention suggests that LLMs may offer a pathway for mapping informal protocols to computable logic, providing preliminary evidence toward scalable clinical trial emulation within the emerging "Symbolic Biomedicine" paradigm championed by the corresponding author.
Code (0)
등록된 구현이 없습니다.
Tasks
Semantic SimilaritySimilar Papers 제목 키워드 기반
ClinicalReTrial: Clinical Trial Redesign with Self-Evolving Agents
Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development (\$2.6B per drug), where protocols are encoded as complex natural language documents, motivating the use of AI syst…
Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks
Interventional cancer clinical trials are generally too restrictive, and some patients are often excluded on the basis of comorbidity, past or concomitant treatments, or the fact that they are over a certain age. The eff…
Representation LearningWord EmbeddingsFrom RAGs to riches: Using large language models to write documents for clinical trials
Clinical trials require numerous documents to be written -- protocols, consent forms, clinical study reports and others. Large language models (LLMs) offer the potential to rapidly generate first versions of these docume…
RAGRetrievalRetrieval-augmented GenerationSECRET: Semi-supervised Clinical Trial Document Similarity Search
Clinical trials are vital for evaluation of safety and efficacy of new treatments. However, clinical trials are resource-intensive, time-consuming and expensive to conduct, where errors in trial design, reduced efficacy,…
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of …