Search Results for author:
Found 6 papers, 1 papers with code
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.
Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping
Cancer is responsible for millions of deaths worldwide every year.
Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features
We applied and compared eight GNN models including AGNN, ChebNet, GAT, GCN, GIN, GraphSAGE, SGC, and TAGCN on the Mayo Clinic cancer disease dataset and assessedtheir performance as well as compared them with each other and with more conventional machinelearning models such as decision tree, gradient boosting, multi-layer perceptron, naive bayes, andrandom forest which we used as the baselines.
Adapting and evaluating a deep learning language model for clinical why-question answering
Objectives: To adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text.
Clinical Concept Extraction: a Methodology Review
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.
Cross-lingual Data Transformation and Combination for Text Classification
In order to train a generalizable model, a large volume of text must be collected.
