Zobrazeno 1 - 10
of 168
pro vyhledávání: '"McInnes, Bridget"'
Autor:
Fu, Yujuan, Ramachandran, Giridhar Kaushik, Halwani, Ahmad, McInnes, Bridget T., Xia, Fei, Lybarger, Kevin, Yetisgen, Meliha, Uzuner, Özlem
Publikováno v:
Journal of the American Medical Informatics Association (2024): ocae231
Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationship between cancer drugs and their associated symptom burden, we extrac
Externí odkaz:
http://arxiv.org/abs/2409.03905
BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction
This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a multi-layer fully connected network architecture, our approach integrat
Externí odkaz:
http://arxiv.org/abs/2405.18605
Publikováno v:
American Medical Informatics Association (AMIA)-2021 Virtual Informatics Summit
Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength,
Externí odkaz:
http://arxiv.org/abs/2104.10791
We hypothesize that explicit integration of contextual information into an Multi-task Learning framework would emphasize the significance of context for boosting performance in jointly learning Named Entity Recognition (NER) and Relation Extraction (
Externí odkaz:
http://arxiv.org/abs/2102.11031
Objective: Neural network de-identification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-worl
Externí odkaz:
http://arxiv.org/abs/2102.08517
Autor:
Mulyar, Andriy, McInnes, Bridget T.
Clinical notes contain an abundance of important but not-readily accessible information about patients. Systems to automatically extract this information rely on large amounts of training data for which their exists limited resources to create. Furth
Externí odkaz:
http://arxiv.org/abs/2004.10220
Autor:
Cuffy, Clint, McInnes, Bridget T.
Publikováno v:
In Journal of Biomedical Informatics July 2023 143
Autor:
French, Evan, McInnes, Bridget T.
Publikováno v:
In Journal of Biomedical Informatics January 2023 137
Publikováno v:
In Forensic Science International: Digital Investigation July 2022 42 Supplement
Publikováno v:
In Journal of Biomedical Informatics June 2022 130