2 citations · 8 across the 18 of their papers we have counts for
18 papers
Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages
Joseph Gatto, Parker Seegmiller, Timothy Burdick +4
Medical triage is the task of allocating medical resources and prioritizing patients based on medical need. This paper introduces the first large-scale public dataset for studying…
How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting
Parker Seegmiller, Joseph Gatto, Sarah E. Greer +4
Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether…
Follow-up Question Generation For Enhanced Patient-Provider Conversations
Joseph Gatto, Parker Seegmiller, Timothy Burdick +3
Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. Conversational contex…
REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction
Omar Sharif, Joseph Gatto, Madhusudan Basak +1
Event argument extraction identifies arguments for predefined event roles in text. Existing work evaluates this task with exact match (EM), where predicted arguments must align exa…
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
Joseph Gatto, Parker Seegmiller, Timothy E. Burdick +1
Since the COVID-19 pandemic, clinicians have seen a large and sustained influx in patient portal messages, significantly contributing to clinician burnout. To the best of our knowl…
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments
Omar Sharif, Joseph Gatto, Madhusudan Basak +1
Prior works formulate the extraction of event-specific arguments as a span extraction problem, where event arguments are explicit -- i.e. assumed to be contiguous spans of text in…
Depth : Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability
Parker Seegmiller, Joseph Gatto, Sarah Masud Preum
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…
Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit
Parker Seegmiller, Joseph Gatto, Omar Sharif +2
Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse. However, the study of using LLMs to model human-l…
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
Joseph Gatto, Madhusudan Basak, Yash Srivastava +2
In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as…
Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types
Joseph Gatto, Parker Seegmiller, Omar Sharif +1
Event Argument Extraction (EAE) is an extremely difficult information extraction problem -- with significant limitations in few-shot cross-domain (FSCD) settings. A common solution…
Chain-of-Thought Embeddings for Stance Detection on Social Media
Joseph Gatto, Omar Sharif, Sarah Masud Preum
Stance detection on social media is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit st…
Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts
Joseph Gatto, Sarah M. Preum
User-generated texts available on the web and social platforms are often long and semantically challenging, making them difficult to annotate. Obtaining human annotation becomes in…
Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity
Joseph Gatto, Omar Sharif, Parker Seegmiller +2
Amidst the sharp rise in the evaluation of large language models (LLMs) on various tasks, we find that semantic textual similarity (STS) has been under-explored. In this study, we…
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints
Parker Seegmiller, Joseph Gatto, Madhusudan Basak +4
Medications often impose temporal constraints on everyday patient activity. Violations of such medical temporal constraints (MTCs) lead to a lack of treatment adherence, in additio…
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
Parker Seegmiller, Joseph Gatto, Abdullah Mamun +4
Prescription medications often impose temporal constraints on regular health behaviors (RHBs) of patients, e.g., eating before taking medication. Violations of such medical tempora…
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse
William Romano, Omar Sharif, Madhusudan Basak +2
Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Proce…
HealthE: Classifying Entities in Online Textual Health Advice
Joseph Gatto, Parker Seegmiller, Garrett Johnston +1
The processing of entities in natural language is essential to many medical NLP systems. Unfortunately, existing datasets vastly under-represent the entities required to model publ…
Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis
Joseph Gatto, Ravi Lanka, Yumi Iwashita +1
Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset? In this paper, we introduce Single Sample Feature Importance (SSFI),…