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Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study

Research Medical/Healthcare AI

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TL;DR - CT-TEL uses LLMs to translate free-text clinical trial protocols into Temporal Ensemble Logic formulas, potentially enabling scalable automated reasoning and trial simulation.

  • The workflow modeled 23 real-world protocols from ClinicalTrials.gov.
  • TEL captures dynamic eligibility criteria and event-timing constraints that unstructured text obscures.
  • Translation fidelity was assessed by back-translating formulas into natural language and comparing semantic similarity with the source.
  • Preliminary semantic-retention results support further study but do not yet establish clinical validity.

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Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study

arXiv cs.LO Yan Huang, Xubing Hao, Xiaojin Li, Rashmie Abeysinghe, Xiaoqian Jiang, Licong Cui, Guo-Qiang Zhang 2026-07-23 arXiv:2607.21307
Public signals Semantic Scholar citations 1 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 1 · Influential citations 0 X · N/A Fetched 2026-08-23 14:28:30.695310 UTC

TL;DR - CT-TEL uses LLMs to translate free-text clinical trial protocols into Temporal Ensemble Logic formulas, potentially enabling scalable automated reasoning and trial simulation.

  • The workflow modeled 23 real-world protocols from ClinicalTrials.gov.
  • TEL captures dynamic eligibility criteria and event-timing constraints that unstructured text obscures.
  • Translation fidelity was assessed by back-translating formulas into natural language and comparing semantic similarity with the source.
  • Preliminary semantic-retention results support further study but do not yet establish clinical validity.
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