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Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

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Extracting Structured Genotype Information from Free-Text HLA Reports Using a Rule-Based Approach.

Kye Hwa Lee¹, Hyo Jung Kim², Yi Jun Kim³

¹Center for Precision Medicine, Seoul National University Hospital, Seoul, Korea. geffa@snu.as.kr.

Journal of Korean Medical Science

|April 2, 2020

Summary

We developed a method to convert unstructured human leukocyte antigen (HLA) genotype data into a structured format. This structured data can unlock the secondary applications of HLA typing results for patient benefit.

Keywords:

Data Sets as Topic Electronic Medical Record Genetic Testing HLA Test Major Histocompatibility Complex

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Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

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Published on: September 6, 2017

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

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

Published on: September 20, 2018

Area of Science:

Bioinformatics
Medical Informatics
Genetics

Background:

Human leukocyte antigen (HLA) typing is crucial for transplant compatibility and disease association studies.
Current HLA typing results are often unstructured (free-text, PDF), limiting their secondary use in electronic medical records.
A method is needed to convert unstructured HLA genotype data into a reusable, structured format.

Purpose of the Study:

To develop and evaluate a rule-based natural language processing (NLP) method for extracting HLA genotype information.
To convert unstructured HLA typing reports into a structured, reusable format.
To improve the accessibility and utility of HLA typing data for secondary applications.

Main Methods:

A rule-based NLP approach using Python regex was employed to query and extract data from HLA typing reports.
The method extracted patient counts, clinical characteristics, and precise HLA genotypes from a large dataset (2000-2018).
Performance was evaluated by comparing extracted results against a manually curated validation set.

Main Results:

The developed rules achieved high precision (0.892-0.999) and recall (0.795-0.998) for extracting HLA genotypes across five genes.
Extraction of patient numbers and clinical variables also demonstrated high accuracy (precision > 0.99, recall > 0.99).
Cleaning rules standardized extracted alleles and serotypes according to formal HLA nomenclature.

Conclusions:

The rule-based method reliably extracts HLA genotype information with high accuracy.
Structured HLA data can be effectively utilized, benefiting patients by unlocking under-used genetic information.
This approach enhances the value of existing clinical data for broader research and diagnostic purposes.