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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.

Chao Pang¹, Annet Sollie², Anna Sijtsma³

¹University of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands, University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands and.

Database : the Journal of Biological Databases and Curation

|September 20, 2015

Summary

Standardizing biomedical data is crucial for research. The SORTA system automates encoding free text or local codes to formal ontologies like SNOMED CT, saving time and improving data quality.

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Area of Science:

Biomedical Informatics
Data Science
Biobanking

Background:

Standardizing biomedical data semantics, such as phenotypes, is essential for comparative and integrative analyses.
Retrospective data standardization is often necessary due to varied data collection protocols.
Manual data curation for matching to ontologies like SNOMED CT, ICD-10, and HPO is time-consuming.

Purpose of the Study:

To develop SORTA, a computer-aided system to mechanize the encoding of free text or locally coded biomedical data to formal coding systems or ontologies.
To evaluate SORTA's applicability and efficiency in real-world use cases.

Main Methods:

SORTA matches original data values to target coding systems (Excel, OWL, OBO formats).
It employs Lucene and n-gram based matching algorithms for semi-automatic candidate code shortlisting.
The system can learn from human expert matches and handles semicolon-delimited data input.

Main Results:

In the LifeLines biobank, SORTA recoded 90,000 free text values to Metabolic Equivalent of Task (MET) codes with 0.97 precision and 0.98 recall.
In the CINEAS system, SORTA mapped to Human Phenotype Ontology (HPO), achieving 0.58 precision and 0.45 recall.
Users reported SORTA as a significant time saver and quality improvement, reducing human error.

Conclusions:

SORTA effectively automates and streamlines the data (re)coding process.
The system demonstrates significant potential to aid numerous projects requiring biomedical data standardization.
SORTA enhances data quality and accelerates research by reducing manual curation efforts.