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Found 11 papers, 3 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.
Transformer Models for Drug Adverse Effects Detection from Tweets
In this paper we present the drug adverse effects detection system developed during our participation in the Social Media Mining for Health Applications Shared Task 2020.
Uncertainty Estimation of Transformer Predictions for Misclassification Detection
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Medical data annotation requires highly qualified expertise.
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction
A rational design of new therapeutic drugs aims to find a molecular structure with desired biological functionality, e. g., an ability to activate or suppress a specific protein via binding to it.
Towards Computationally Feasible Deep Active Learning
Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models.
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.
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.
Noise-Resilient Automatic Interpretation of Holter ECG Recordings
Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient.
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.
