Search Results for author:
Found 7 papers, 3 papers with code
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.
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.
MedSTS: A Resource for Clinical Semantic Textual Similarity
A subset of MedSTS (MedSTS_ann) containing 1, 068 sentence pairs was annotated by two medical experts with semantic similarity scores of 0-5 (low to high similarity).
MedTQ: Dynamic Topic Discovery and Query Generation for Medical Ontologies
Given a predicate similarity metric, machine learning algorithms have been developed for automatic topic discovery and query generation.
A Comparison of Word Embeddings for the Biomedical Natural Language Processing
First, the word embeddings trained on clinical notes and biomedical publications can capture the semantics of medical terms better, and find more relevant similar medical terms, and are closer to human experts' judgments, compared to these trained on Wikipedia and news.
Information Retrieval
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications
We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models.
