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Found 54 papers, 28 papers with code
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query.
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
This approach has the promise to empower physicians to use their domain expertise to craft features which are clinically meaningful for a downstream task of interest, without having to manually extract these from raw EHR (as often done now).
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved.
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 $\mathcal{S}_t$ such that if the instances comprising $\mathcal{S}_t$ had been removed prior to training, the categorization of a given test point $x_t$ would have been different.
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?
Intermediate Entity-based Sparse Interpretable Representation Learning
However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training.
Influence Functions for Sequence Tagging Models
We show the practical utility of segment influence by using the method to identify systematic annotation errors in two named entity recognition corpora.
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.
Self-Repetition in Abstractive Neural Summarizers
In a regression analysis, we find that the three architectures have different propensities for repeating content across output summaries for inputs, with BART being particularly prone to self-repetition.
That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data
We compare alignments from a state-of-the-art multimodal (image and text) model for EHR with human annotations that link image regions to sentences.
Learning to Ask Like a Physician
The questions are generated by medical experts from 100+ MIMIC-III discharge summaries.
Evaluating Factuality in Text Simplification
We find that errors often appear in both that are not captured by existing evaluation metrics, motivating a need for research into ensuring the factual accuracy of automated simplification models.
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.
Combining Feature and Instance Attribution to Detect Artifacts
In this paper we evaluate use of different attribution methods for aiding identification of training data artifacts.
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i. e., the release of pretrained models such as ClinicalBERT.
Disentangling Representations of Text by Masking Transformers
Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a multitude of downstream tasks.
On the Impact of Random Seeds on the Fairness of Clinical Classifiers
Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s).
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.
An Empirical Comparison of Instance Attribution Methods for NLP
Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction.
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation.
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.
Semi-Automating Knowledge Base Construction for Cancer Genetics
We propose and evaluate several model variants, including a transformer-based joint entity and relation extraction model to extract <germline mutation, risk-estimate>} pairs.
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
In this work, we investigate the use of influence functions for NLP, providing an alternative approach to interpreting neural text classifiers.
Evidence Inference 2.0: More Data, Better Models
Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions.
Learning to Faithfully Rationalize by Construction
In NLP this often entails extracting snippets of an input text `responsible for' corresponding model output; when such a snippet comprises tokens that indeed informed the model's prediction, it is a faithful explanation.
Query-Focused EHR Summarization to Aid Imaging Diagnosis
We propose and evaluate models that extract relevant text snippets from patient records to provide a rough case summary intended to aid physicians considering one or more diagnoses.
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
We examine these questions by contrasting the performance of several variants of LSTM-CRF architectures for named entity recognition, with some provided only representations of the context as features.
Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models
We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities.
ERASER: A Benchmark to Evaluate Rationalized NLP Models
We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i. e., the degree to which provided rationales influenced the corresponding predictions).
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period.
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing.
An Analysis of Attention over Clinical Notes for Predictive Tasks
In this work we perform experiments to explore this question using two EMR corpora and four different predictive tasks, that: (i) inclusion of attention mechanisms is critical for neural encoder modules that operate over notes fields in order to yield competitive performance, but, (ii) unfortunately, while these boost predictive performance, it is decidedly less clear whether they provide meaningful support for predictions.
Inferring Which Medical Treatments Work from Reports of Clinical Trials
In this paper, we present a new task and corpus for making this unstructured evidence actionable.
Structured Neural Topic Models for Reviews
We present Variational Aspect-based Latent Topic Allocation (VALTA), a family of autoencoding topic models that learn aspect-based representations of reviews.
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).
Practical Obstacles to Deploying Active Learning
Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget.
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.
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness
In this paper, we present a method that retrofits distributional context vector representations of biomedical concepts using structural information from the UMLS Metathesaurus, such that the similarity between vector representations of linked concepts is augmented.
Quantifying Mental Health from Social Media with Neural User Embeddings
Our experimental results demonstrate that the user embeddings capture similarities between users with respect to mental conditions, and are predictive of mental health.
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization
A fundamental advantage of neural models for NLP is their ability to learn representations from scratch.
Neural Information Retrieval: A Literature Review
A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing.
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
We introduce a deep neural network for automated sarcasm detection.
Active Discriminative Text Representation Learning
We also show that, as expected, the method quickly learns discriminative word embeddings.
Rationale-Augmented Convolutional Neural Networks for Text Classification
We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their component sentences.
