23 citations · 65 across the 13 of their papers we have counts for
14 papers · 1 filter
Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?
Hye Sun Yun, Karen Y. C. Zhang, Ramez Kouzy +3
Medical research faces well-documented challenges in translating novel treatments into clinical practice. Publishing incentives encourage researchers to present "positive" findings…
Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models
Hye Sun Yun, David Pogrebitskiy, Iain J. Marshall +1
Meta-analyses statistically aggregate the findings of different randomized controlled trials (RCTs) to assess treatment effectiveness. Because this yields robust estimates of treat…
Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)
Chantal Shaib, Millicent L. Li, Sebastian Joseph +3
Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in few- and zero-shot settings. However, it is unclear if such…
Appraising the Potential Uses and Harms of LLMs for Medical Systematic Reviews
Hye Sun Yun, Iain J. Marshall, Thomas A. Trikalinos +1
Medical systematic reviews play a vital role in healthcare decision making and policy. However, their production is time-consuming, limiting the availability of high-quality and up…
Do Multi-Document Summarization Models Synthesize?
Jay DeYoung, Stephanie C. Martinez, Iain J. Marshall +1
Multi-document summarization entails producing concise synopses of collections of inputs. For some applications, the synopsis should accurately synthesize inputs with respect to a…
What Would it Take to get Biomedical QA Systems into Practice?
Gregory Kell, Iain J. Marshall, Byron C. Wallace +1
Medical question answering (QA) systems have the potential to answer clinicians uncertainties about treatment and diagnosis on demand, informed by the latest evidence. However, des…
Paragraph-level Simplification of Medical Texts
Ashwin Devaraj, Iain J. Marshall, Byron C. Wallace +1
We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practica…
Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations
Benjamin E. Nye, Jay DeYoung, Eric Lehman +3
The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manual…
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization
Byron C. Wallace, Sayantan Saha, Frank Soboczenski +1
We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarizat…
Evidence Inference 2.0: More Data, Better Models
Jay DeYoung, Eric Lehman, Ben Nye +2
How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no s…
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
Benjamin Nye, Junyi Jessy Li, Roma Patel +4
We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that de…
Syntactic Patterns Improve Information Extraction for Medical Search
Roma Patel, Yinfei Yang, Iain Marshall +2
Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest. In this paper we demon…
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Sarthak Jain, Edward Banner, Jan-Willem van de Meent +2
We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and inte…
Rationale-Augmented Convolutional Neural Networks for Text Classification
Ye Zhang, Iain Marshall, Byron C. Wallace
We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their component sentences. Specifically, we consider…