2 citations · 4 across the 5 of their papers we have counts for
6 papers
Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning
Félicien Hêche, Sohrab Ferdowsi, Anthony Yazdani +2
Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of…
ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohorts
Dimitrios Proios, Alban Bornet, Anthony Yazdani +2
Patient stratification identifying clinically meaningful subgroups is essential for advancing personalized medicine through improved diagnostics and treatment strategies. Electroni…
GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition
Anthony Yazdani, Ihor Stepanov, Douglas Teodoro
Biomedical named entity recognition (NER) presents unique challenges due to specialized vocabularies, the sheer volume of entities, and the continuous emergence of novel entities.…
An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
Anthony Yazdani, Alban Bornet, Philipp Khlebnikov +4
Adverse drug events (ADEs) are a major safety issue in clinical trials. Thus, predicting ADEs is key to developing safer medications and enhancing patient outcomes. To support this…
DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using Sentence Transformers and Reciprocal-Rank Fusion
Anthony Yazdani, Hossein Rouhizadeh, David Vicente Alvarez +1
This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health (DS4DH) group for the Social Media…
Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events
Anthony Yazdani, Dimitrios Proios, Hossein Rouhizadeh +1
Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale el…