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Found 17 papers, 3 papers with code
Follow-up Question Generation For Enhanced Patient-Provider Conversations
These two challenges occur frequently in medical dialogue as a doctor asks questions based not only on patient utterances but also their prior EHR data and current diagnostic hypotheses.
REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction
To bridge this gap, we introduce Reliable Evaluation framework for Generative event argument extraction (REGen), a framework that better aligns with human judgment.
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
We believe this work provides a path forward for (i) the release of large-scale synthetic patient message datasets that are stylistically similar to ground-truth samples and (ii) HIPAA-friendly data generation which requires minimal human de-identification efforts.
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments
First, implicit arguments are event arguments which are not explicitly mentioned in the text, but can be inferred through context.
Depth $F_1$: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability
Recent evaluations of cross-domain text classification models aim to measure the ability of a model to obtain domain-invariant performance in a target domain given labeled samples in a source domain.
Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit
Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse.
Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types
A common solution to FSCD modeling is data augmentation.
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
We detail (i) a method of claim identification -- the task of identifying if a post title contains a claim and (ii) an opinion mining-driven evaluation framework for stance detection using LLMs.
Chain-of-Thought Embeddings for Stance Detection on Social Media
Chain-of-Thought (COT) prompting has recently been shown to improve performance on stance detection tasks -- alleviating some of these issues.
Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts
Our error analysis shows that AMR-infused language models perform better on complex texts and generally show less predictive variance in the presence of changing complexity.
Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity
Additionally, we show generative LLMs significantly outperform existing encoder-based STS models when characterizing the semantic similarity between two texts with complex semantic relationships dependent on world knowledge.
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints
Medications often impose temporal constraints on everyday patient activity.
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse
Lastly, we found that a large language model (ChatGPT) outperforms unsupervised keyphrase extraction models, and we evaluate its efficacy in this task.
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
It also addresses the challenges of accurately predicting RHBs central to MTCs (e. g., medication intake).
HealthE: Classifying Entities in Online Textual Health Advice
The processing of entities in natural language is essential to many medical NLP systems.
Scope of Pre-trained Language Models for Detecting Conflicting Health Information
This is the task of health conflict detection (HCD).
Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis
Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset?
