# pharm-relation-extraction

Model trained to recognize 4 types of relationships between significant pharmacological entities in russian-language reviews: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. The input of the model is a review text and a pair of entities, between which it is required to determine the fact of a relationship and one of the 4 types of relationship, listed above.

## Data

Proposed model is trained on a subset of 908 reviews of the Russian Drug Review Corpus (RDRS). The subset contains the pairs of entities marked with the 4 listed types of relationships:

• ADR-Drugname — the relationship between the drug and its side effects
• Drugname-SourceInfodrug — the relationship between the medication and the source of information about it (e.g., “was advised at the pharmacy”, e.g., “was advised at the pharmacy”, “the doctor recommended it”);
• Drugname-Diseasname — the relationship between the drug and the disease
• Diseasename-Indication — the connection between the illness and its symptoms (e.g., “cough”, “fever 39 degrees”) Also, this subset contains pairs of the same entity types between which there is no relationship: for example, a drug and an unrelated side effect that appeared after taking another drug; in other words, this side effect is related to another drug.

## Model topology and training

Proposed model is based on the XLM-RoBERTA-large topology. After the additional training as a language model on corpus of unmarked drug reviews, this model was trained as a classification model on 80% of the texts from subset of the corps described above.

## How to use

See section "How to use" in our git repository for the model

## Results

Here are the accuracy, estimated by the f1 score metric for the recognition of relationships on the best fold.

0.955 0.892 0.922 0.891

## Citation info

If you have found our results helpful in your work, feel free to cite our publication as:

@article{sboev2021extraction,
title={Extraction of the Relations between Significant Pharmacological Entities in Russian-Language Internet Reviews on Medications},
author={Sboev, Alexander and Selivanov, Anton and Moloshnikov, Ivan and Rybka, Roman and Gryaznov, Artem and Sboeva, Sanna and Rylkov, Gleb},
year={2021},
publisher={Preprints}
}