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Found 10 papers, 6 papers with code
Word centrality constrained representation for keyphrase extraction
Keyphrases provide a concise representation by identifying salient concepts in a document.
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving.
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This work aims to serve as a valuable resource for understanding the potential and opportunities of HKG in health research.
Neighborhood-Regularized Self-Training for Learning with Few Labels
Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.
GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING
We also empirically show that GDA-AM solves a variety of minimax problems and improves GAN training on several datasets
You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions
We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user{'}s sentiment towards an entity from the conversation context, and 2) transforms the ratings of {``}similar{''} external reviewers to predict the current user{'}s preferences.
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor.
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
To tackle these challenges, we propose a {\it CO}nstrained {\it PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors.
ACDC: $α$-Carving Decision Chain for Risk Stratification
The resulting chain of decision rules yields a pure subset of the minority class examples.
