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Improving Translational Accuracy

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 Accuracy

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 14, 2026

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

Published on: June 6, 2025

Using a pipeline to improve de-identification performance.

Frances P Morrison¹, Soumitra Sengupta, George Hripcsak

¹Columbia University Department of Biomedical Informatics.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 31, 2010

Summary

This summary is machine-generated.

Pipelining two text-processing systems, deid.pl and MedLEE, effectively de-identified clinical notes, retaining only 2% of protected health information (PHI). This strategy enhances electronic health record data reuse for research.

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Area of Science:

Medical Informatics
Natural Language Processing
Health Data Security

Background:

Electronic health records (EHRs) contain valuable data for research.
De-identification is crucial for protecting patient privacy and enabling data reuse.
Existing de-identification methods require optimization for accuracy and efficiency.

Purpose of the Study:

To evaluate a novel de-identification strategy using a pipeline of two text-processing systems.
To assess the effectiveness of combining deid.pl and MedLEE for removing protected health information (PHI) from clinical notes.
To determine the error rate and nature of retained PHI in de-identified clinical notes.

Main Methods:

A pipeline approach was implemented, processing 100 outpatient clinical notes sequentially through deid.pl (PhysioToolkit) and then MedLEE.
Manual comparison of the de-identified output against original notes was performed to identify retained PHI.
Error rates and types of retained personal health information were analyzed.

Main Results:

The pipelined system achieved a low overall error rate of 2% in de-identifying clinical notes.
Only two personal names were retained in the output, one initial and a common medical term.
All retained PHI was successfully transformed into standardized medical concepts, reducing re-identification risk.

Conclusions:

Pipelining deid.pl with MedLEE significantly improved the exclusion of PHI from de-identified clinical notes.
This tandem text-processing strategy offers a promising approach for de-identifying clinical data.
The method provides computer-readable output, facilitating the reuse of electronic health record data for research purposes.