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20192023
most citedE.T.-RNN: Applying Deep Learning to Credit Loan Applications

91 citations · 103 across the 7 of their papers we have counts for

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cs.CL20234 cited

Deciphering Diagnoses: How Large Language Models Explanations Influence Clinical Decision Making

D. Umerenkov, G. Zubkova, A. Nesterov

Clinical Decision Support Systems (CDSS) utilize evidence-based knowledge and patient data to offer real-time recommendations, with Large Language Models (LLMs) emerging as a promi…

cs.CL2023

Predicting COVID-19 and pneumonia complications from admission texts

Dmitriy Umerenkov, Oleg Cherkashin, Alexander Nesterov +4

In this paper we present a novel approach to risk assessment for patients hospitalized with pneumonia or COVID-19 based on their admission reports. We applied a Longformer neural n…

cs.CL20225 cited

RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining

Alexander Yalunin, Alexander Nesterov, Dmitriy Umerenkov

This paper presents several BERT-based models for Russian language biomedical text mining (RuBioBERT, RuBioRoBERTa). The models are pre-trained on a corpus of freely available text…

cs.CL20223 cited

Abstractive summarization of hospitalisation histories with transformer networks

Alexander Yalunin, Dmitriy Umerenkov, Vladimir Kokh

In this paper we present a novel approach to abstractive summarization of patient hospitalisation histories. We applied an encoder-decoder framework with Longformer neural network…

cs.CL2022

Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

Alexander Nesterov, Dmitry Umerenkov

Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts processing. Usually Medical EE is a two-step process: named entity recognition (NER) a…