Deep learning models in detection of dietary supplement adverse event signals from Twitter
Objective The objective of this study is to develop a deep learning pipeline to detect signals
on dietary supplement-related adverse events (DS AEs) from Twitter. Materials and Methods …
on dietary supplement-related adverse events (DS AEs) from Twitter. Materials and Methods …
Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need
Objective To solve major clinical natural language processing (NLP) tasks using a unified
text-to-text learning architecture based on a generative large language model (LLM) via …
text-to-text learning architecture based on a generative large language model (LLM) via …
[PDF][PDF] Guided Attention Network for Concept Extraction.
Abstract Concept extraction aims to find words or phrases describing a concept from
massive texts. Recently, researchers propose many neural network-based methods to …
massive texts. Recently, researchers propose many neural network-based methods to …
Generative entity typing with curriculum learning
Entity typing aims to assign types to the entity mentions in given texts. The traditional
classification-based entity typing paradigm has two unignorable drawbacks: 1) it fails to …
classification-based entity typing paradigm has two unignorable drawbacks: 1) it fails to …
In-domain pre-training improves clinical note generation from doctor-patient conversations
Summarization of doctor-patient conversations into clinical notes by medical scribes is an
essential process for effective clinical care. Pre-trained transformer models have shown a …
essential process for effective clinical care. Pre-trained transformer models have shown a …
Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias
Objective To develop a natural language processing (NLP) package to extract social
determinants of health (SDoH) from clinical narratives, examine the bias among race and …
determinants of health (SDoH) from clinical narratives, examine the bias among race and …
[HTML][HTML] A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora
Background Clinical trial protocols are the foundation for advancing medical sciences,
however, the extraction of accurate and meaningful information from the original clinical …
however, the extraction of accurate and meaningful information from the original clinical …
SIMD Dataflow Co-optimization for Efficient Neural Networks Inferences on CPUs
C Zhou, Z Hassman, R Xu, D Shah, V Richard… - arXiv preprint arXiv …, 2023 - arxiv.org
We address the challenges associated with deploying neural networks on CPUs, with a
particular focus on minimizing inference time while maintaining accuracy. Our novel …
particular focus on minimizing inference time while maintaining accuracy. Our novel …
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and
survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving …
survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving …
Extracting adverse drug events from clinical Notes: A systematic review of approaches used
S Modi, KA Kasmiran, NM Sharef… - Journal of Biomedical …, 2024 - Elsevier
Background An adverse drug event (ADE) is any unfavorable effect that occurs due to the
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …