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09/08/2023
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
Unstructured Electronic Health Record (EHR) data often contains critical...
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06/20/2023
Evaluating the Zero-shot Robustness of Instruction-tuned Language Models
Instruction fine-tuning has recently emerged as a promising approach for...
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05/23/2023
USB: A Unified Summarization Benchmark Across Tasks and Domains
An abundance of datasets exist for training and evaluating models on the...
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05/21/2023
Multilingual Simplification of Medical Texts
Automated text simplification aims to produce simple versions of complex...
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05/19/2023
Appraising the Potential Uses and Harms of LLMs for Medical Systematic Reviews
Medical systematic reviews are crucial for informing clinical decision m...
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05/10/2023
Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)
Large language models, particularly GPT-3, are able to produce high qual...
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05/08/2023
Revisiting Relation Extraction in the era of Large Language Models
Relation extraction (RE) is the core NLP task of inferring semantic rela...
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05/05/2023
Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs
Results from Randomized Controlled Trials (RCTs) establish the comparati...
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03/07/2023
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
We present TrialsSummarizer, a system that aims to automatically summari...
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02/23/2023
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
Large Language Models (LLMs) have yielded fast and dramatic progress in ...
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02/11/2023
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
Accessing medical literature is difficult for laypeople as the content i...
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02/04/2023
How Many and Which Training Points Would Need to be Removed to Flip this Prediction?
We consider the problem of identifying a minimal subset of training data...
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01/31/2023
Do Multi-Document Summarization Models Synthesize?
Multi-document summarization entails producing concise synopses of colle...
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12/03/2022
Intermediate Entity-based Sparse Interpretable Representation Learning
Interpretable entity representations (IERs) are sparse embeddings that a...
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10/25/2022
Influence Functions for Sequence Tagging Models
Many language tasks (e.g., Named Entity Recognition, Part-of-Speech tagg...
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10/22/2022
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
The primary goal of drug safety researchers and regulators is to promptl...
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10/14/2022
Self-Repetition in Abstractive Neural Summarizers
We provide a quantitative and qualitative analysis of self-repetition in...
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06/06/2022
Learning to Ask Like a Physician
Existing question answering (QA) datasets derived from electronic health...
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04/15/2022
Evaluating Factuality in Text Simplification
Automated simplification models aim to make input texts more readable. S...
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09/21/2021
What Would it Take to get Biomedical QA Systems into Practice?
Medical question answering (QA) systems have the potential to answer cli...
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07/01/2021
Combining Feature and Instance Attribution to Detect Artifacts
Training the large deep neural networks that dominate NLP requires large...
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06/17/2021
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that off...
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04/15/2021
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
Large Transformers pretrained over clinical notes from Electronic Health...
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04/14/2021
Disentangling Representations of Text by Masking Transformers
Representations from large pretrained models such as BERT encode a range...
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04/13/2021
On the Impact of Random Seeds on the Fairness of Clinical Classifiers
Recent work has shown that fine-tuning large networks is surprisingly se...
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04/12/2021
Paragraph-level Simplification of Medical Texts
We consider the problem of learning to simplify medical texts. This is i...
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04/09/2021
An Empirical Comparison of Instance Attribution Methods for NLP
Widespread adoption of deep models has motivated a pressing need for app...
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10/22/2020
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
Unsupervised Data Augmentation (UDA) is a semi-supervised technique that...
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10/07/2020
Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations
The best evidence concerning comparative treatment effectiveness comes f...
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08/25/2020
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization
We consider the problem of automatically generating a narrative biomedic...
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05/21/2020
Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time
We introduce Trialstreamer, a living database of clinical trial reports....
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05/17/2020
Semi-Automating Knowledge Base Construction for Cancer Genetics
In this work, we consider the exponentially growing subarea of genetics ...
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05/14/2020
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Modern deep learning models for NLP are notoriously opaque. This has mot...
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05/08/2020
Evidence Inference 2.0: More Data, Better Models
How do we most effectively treat a disease or condition? Ideally, we cou...
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04/30/2020
Learning to Faithfully Rationalize by Construction
In many settings it is important for one to be able to understand why a ...
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04/09/2020
Query-Focused EHR Summarization to Aid Imaging Diagnosis
Electronic Health Records (EHRs) provide vital contextual information to...
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04/09/2020
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
Named Entity Recognition systems achieve remarkable performance on domai...
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04/08/2020
Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models
Named entity recognition systems perform well on standard datasets compr...
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11/08/2019
ERASER: A Benchmark to Evaluate Rationalized NLP Models
State-of-the-art models in NLP are now predominantly based on deep neura...
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06/28/2019
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
Hypertension is a major risk factor for stroke, cardiovascular disease, ...
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05/19/2019
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
Modern NLP systems require high-quality annotated data. In specialized d...
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04/05/2019
An Analysis of Attention over Clinical Notes for Predictive Tasks
The shift to electronic medical records (EMRs) has engendered research i...
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04/02/2019
Inferring Which Medical Treatments Work from Reports of Clinical Trials
How do we know if a particular medical treatment actually works? Ideally...
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02/26/2019
Attention is not Explanation
Attention mechanisms have seen wide adoption in neural NLP models. In ad...
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12/12/2018
Structured Neural Topic Models for Reviews
We present Variational Aspect-based Latent Topic Allocation (VALTA), a f...
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12/12/2018
Structured Representations for Reviews:Aspect-Based Variational Hidden Factor Models
We present Variational Aspect-Based Latent Dirichlet Allocation (VALDA),...
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10/02/2018
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
We propose a model for tagging unstructured texts with an arbitrary numb...
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07/12/2018
How transferable are the datasets collected by active learners?
Active learning is a widely-used training strategy for maximizing predic...
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06/11/2018
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 artic...
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04/19/2018
