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Varvara Koshman^1,2, Anastasia Funkner^1,2, Sergey Kovalchuk^1,2

¹Federal Almazov North-West Medical Research Centre, Center for Artificial Intelligence Development in Medicine, 197341 Saint Petersburg, Russia.

Journal of Personalized Medicine

|January 21, 2022

Summary

This summary is machine-generated.

This study introduces an unsupervised method for automatically annotating Russian electronic medical records (EMRs). The approach effectively labels unstructured EMR data, enabling machine learning model training in the biomedical domain.

Keywords:

Node2Vec automatic text labeling electronic health records graph algorithms natural language processing syntactical parsing

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Area of Science:

Medical Informatics
Natural Language Processing
Computational Linguistics

Background:

Electronic medical records (EMRs) contain valuable patient data, but it is largely unstructured.
A scarcity of labeled Russian medical text data and annotation tools hinders machine learning applications in the biomedical field.
Researchers face challenges utilizing EMR text data for training biomedical machine learning models due to annotation limitations.

Purpose of the Study:

To develop an unsupervised method for automatic annotation of Russian electronic medical records (EMRs).
To address the lack of labeled medical text data and annotation tools for Russian EMRs.
To facilitate the use of EMR text data in training machine learning models for the biomedical domain.

Main Methods:

Generating syntactic trees from sentences via morphological and syntactical analysis.
Grouping similar subtrees using Node2Vec and Word2Vec algorithms.
Labeling grouped subtrees with domain vocabularies and Wikidata categories.

Main Results:

The integration of Wikidata categories enhanced sentence labeling by 5.5 times compared to using only domain vocabularies.
The proposed labeling method achieved 92.7% accuracy in generating meaningful labels for grouped subtrees on a validation dataset.
Annotation using domain vocabularies and Wikidata categories covered over 82% of the corpus, reaching 97% with added timestamp and event labels.

Conclusions:

The developed unsupervised method enables automatic labeling of Russian EMRs.
The methodology is adaptable for other languages lacking resources for automatic annotation and domain vocabularies.
This approach significantly improves the accessibility and usability of EMR data for machine learning research.