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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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RNA-seq

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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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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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Published on: September 20, 2018

Using SNOMED-CT to encode summary level data - a corpus analysis.

Hongfang Liu1, Kavishwar Wagholikar, Stephen Tze-Inn Wu

  • 1Department of Health Sciences Research Mayo Clinic College of Medicine, Rochester, MN.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|July 11, 2012
PubMed
Summary
This summary is machine-generated.

Encoding summary level clinical data from electronic medical records (EMRs) using SNOMED-CT is effective. Approximately 96% of summary data can be encoded, supporting clinical research and decision support.

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Area of Science:

  • Medical Informatics
  • Clinical Terminology
  • Health Data Standards

Background:

  • Electronic medical records (EMRs) contain vital clinical information.
  • Secondary use of EMRs is crucial for decision support, patient safety, and research.
  • Summary-level information (problem lists, diagnoses) is a key component of EMR free text.

Purpose of the Study:

  • To systematically analyze the capability of SNOMED-CT in representing summary-level clinical information.
  • To evaluate the feasibility of encoding itemized summary data using SNOMED-CT concepts.

Main Methods:

  • Conducted a systematic analysis of SNOMED-CT.
  • Utilized a large collection of itemized summary-level data for evaluation.
  • Assessed encoding rates using normalized SNOMED-CT phrases and concepts.

Main Results:

  • Approximately 80% of summary-level entries were successfully encoded using SNOMED-CT normalized phrases.
  • Encoding success increased to 96% when allowing for one unmapped token.
  • Demonstrated SNOMED-CT's robust capability in representing diverse summary-level clinical data.

Conclusions:

  • SNOMED-CT is highly effective for encoding summary-level clinical information from EMRs.
  • The findings provide a strong basis for developing automated systems for clinical data encoding.
  • Enhanced encoding of summary data can facilitate improved clinical decision support and research.