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An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic

Varvara Koshman¹, Anastasia Funkner¹, Sergey Kovalchuk^1,2

¹ITMO University, Saint Petersburg, Russia.

Studies in Health Technology and Informatics

|November 4, 2021

Summary

This summary is machine-generated.

This study introduces an unsupervised method for automatically annotating Russian Electronic Medical Records (EMR). The approach leverages syntactic analysis and Word2Vec to label unstructured medical data, addressing a critical resource gap.

Keywords:

automatic text labeling electronic health records graph algorithms natural language processing node2vec syntactical parsing

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Area of Science:

Natural Language Processing
Medical Informatics
Computational Linguistics

Background:

Electronic Medical Records (EMR) contain unstructured patient data.
A scarcity of labeled Russian medical text and annotation tools exists.
Automatic annotation is crucial for unlocking EMR data value.

Purpose of the Study:

To develop an unsupervised approach for automatic medical data annotation.
To address the lack of labeled Russian medical text data.
To create tools for annotating Electronic Medical Records.

Main Methods:

Utilizing morphological and syntactical analysis to generate syntactic trees.
Grouping similar subtrees using Word2Vec embeddings.
Labeling grouped subtrees with dictionaries and Wikidata categories.

Main Results:

Demonstrated an unsupervised method for medical data annotation.
Successfully applied the methodology to Russian EMR data.
Generated labeled medical text without prior supervised data.

Conclusions:

The proposed unsupervised method enables automatic labeling of Russian EMRs.
This methodology is adaptable to other languages lacking resources.
It facilitates the utilization of unstructured medical data for research and clinical insights.