Search Results for author:
Found 12 papers, 9 papers with code
Appraising the Potential Uses and Harms of LLMs for Medical Systematic Reviews
We conducted 16 interviews with international systematic review experts to characterize the perceived utility and risks of LLMs in the specific context of medical evidence reviews.
Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)
Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in few- and zero-shot settings.
Do Multi-Document Summarization Models Synthesize?
In this paper we ask: To what extent do modern multi-document summarization models implicitly perform this type of synthesis?
What Would it Take to get Biomedical QA Systems into Practice?
Medical question answering (QA) systems have the potential to answer clinicians uncertainties about treatment and diagnosis on demand, informed by the latest evidence.
Paragraph-level Simplification of Medical Texts
In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics.
Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations
Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction).
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization
We enlist medical professionals to evaluate generated summaries, and we find that modern summarization systems yield consistently fluent and relevant synopses, but that they are not always factual.
Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time
We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic.
Evidence Inference 2.0: More Data, Better Models
Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions.
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i. e., an ontology).
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
We present a corpus of 5, 000 richly annotated abstracts of medical articles describing clinical randomized controlled trials.
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
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 interpretability.
