Search Results for author:
Found 13 papers, 4 papers with code
RuCCoN: Clinical Concept Normalization in Russian
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Medical data annotation requires highly qualified expertise.
Combining Survival Analysis and Machine Learning for Mass Cancer Risk Prediction using EHR data
We test the Survival Ensemble in some studies.
Predicting COVID-19 and pneumonia complications from admission texts
In this paper we present a novel approach to risk assessment for patients hospitalized with pneumonia or COVID-19 based on their admission reports.
Whole-body PET image denoising for reduced acquisition time
This paper evaluates the performance of supervised and unsupervised deep learning models for denoising positron emission tomography (PET) images in the presence of reduced acquisition times.
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization
The symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition.
Abstractive summarization of hospitalisation histories with transformer networks
In this paper we present a novel approach to abstractive summarization of patient hospitalisation histories.
RuMedBench: A Russian Medical Language Understanding Benchmark
The paper describes the open Russian medical language understanding benchmark covering several task types (classification, question answering, natural language inference, named entity recognition) on a number of novel text sets.
Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
The COVID-19 pandemic created a significant interest and demand for infection detection and monitoring solutions.
Medical Profile Model: Scientific and Practical Applications in Healthcare
The paper researches the problem of representation learning for electronic health records.
CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning
Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19. Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizingpatients by severity of the disease.
Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks
We show the importance of this problem in medical community and present comprehensive historical review of the problem and proposed methods.
Radiologist-level stroke classification on non-contrast CT scans with Deep U-Net
Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke.
