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Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Video

Updated: Jun 5, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping

Chao Yan1, Yi Xin2, Wu-Chen Su1

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Medrxiv : the Preprint Server for Health Sciences
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Large language models show promise for translating clinical definitions into electronic health record (EHR) database queries. However, documentation quality, not model performance, remains a key challenge for EHR research.

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Published on: June 13, 2025

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Data Management

Background:

  • Translating clinical definitions into executable electronic health record (EHR) database queries is crucial for research but is a labor-intensive process.
  • The increasing availability of EHR data necessitates efficient methods for phenotype extraction and query generation.

Purpose of the Study:

  • To evaluate the performance of two advanced large language models (LLMs) in generating EHR database queries from clinical definitions.
  • To assess the impact of different documentation modalities (e.g., structured text, diagrams) on LLM performance for EHR phenotype translation.

Main Methods:

  • Two state-of-the-art large language models were tested on five distinct clinical phenotypes.
  • The models processed three types of documentation: structured text, semi-structured text, and diagrams.
  • Performance was evaluated based on the accuracy and completeness of the generated EHR database queries.

Main Results:

  • Both evaluated LLMs demonstrated proficiency in capturing high-level logic from structured and semi-structured clinical documentation.
  • Model performance significantly degraded when presented with diagram-only input, indicating limitations in interpreting visual data.
  • Error analysis identified seven distinct categories of failures, with documentation quality emerging as the primary bottleneck.

Conclusions:

  • While LLMs show potential for automating EHR phenotype query generation, current capabilities are constrained by input documentation quality.
  • Standardization of clinical documentation and continued expert oversight are essential to overcome current limitations and improve LLM utility in EHR research.
  • Future research should focus on improving LLM's ability to interpret diverse documentation formats and addressing the impact of data quality.