Search Results for author:
Found 9 papers, 0 papers with code
Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs
Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation.
Clinical Trial Recommendations Using Semantics-Based Inductive Inference and Knowledge Graph Embeddings
We addressed several important research questions in this context, including designing a knowledge graph (KG) for clinical trial data, effectiveness of various KG embedding (KGE) methods for it, a novel inductive inference using KGE, and its use in generating recommendations for clinical trial design.
Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation
We propose custom2vec, an algorithmic framework to customize graph embeddings by incorporating user preferences in training the embeddings.
Clinical Trial Information Extraction with BERT
Natural language processing (NLP) of clinical trial documents can be useful in new trial design.
A Scalable AI Approach for Clinical Trial Cohort Optimization
FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria.
The impact of external innovation on new drug approvals: A retrospective analysis
Pharmaceutical companies are relying more often on external sources of innovation to boost their discovery research productivity.
Applications of artificial intelligence in drug development using real-world data
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development.
Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing
COVID-19 clinical trial design is a critical task in developing therapeutics for the prevention and treatment of COVID-19.
Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning
We then use the trained models to classify and predict potentially high-risk admissions/patients.
