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Found 8 papers, 0 papers with code
Multi-Task Pharmacovigilance Mining from Social Media Posts
Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported.
Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction
Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients.
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare
Distributed representations have been used to support downstream tasks in healthcare recently.
Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings
Distributed representations of medical concepts have been used to support downstream clinical tasks recently.
Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering
Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge.
Improving Medical NLI Using Context-Aware Domain Knowledge
Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training.
Ensemble Fine-tuned mBERT for Translation Quality Estimation
Quality Estimation (QE) is an important component of the machine translation workflow as it assesses the quality of the translated output without consulting reference translations.
Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction
In this study, we investigated the potential of GPT-3 for the anti-cancer drug sensitivity prediction task using structured pharmacogenomics data across five tissue types and evaluated its performance with zero-shot prompting and fine-tuning paradigms.
