Skip to search formSkip to main contentSkip to account menu

MIMIC-III, a freely accessible critical care database

MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care…
View on Springer (opens in a new tab)

Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

TextFooler is presented, a simple but strong baseline to generate adversarial text that outperforms previous attacks by success rate and perturbation rate, and is utility-preserving and efficient, which generates adversarialtext with computational complexity linear to the text length.
View PDF on arXiv (opens in a new tab)

What Is a Knowledge Representation?

It is argued that keeping in mind all five of these roles that a representation plays provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.
View via Publisher (opens in a new tab)

Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment

The TextFooler is presented, a general attack framework, to generate natural adversarial texts that outperforms state-of-the-art attacks in terms of success rate and perturbation rate.

De-identification of patient notes with recurrent neural networks

The first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems, is introduced, which outperforms the state-of-the-art systems.
View on Wolters Kluwer (opens in a new tab)

Viewpoint Paper: Evaluating the State-of-the-Art in Automatic De-identification

An overview of this de-identification challenge is provided, the data and the annotation process are described, the evaluation metrics are explained, the nature of the systems that addressed the challenge are discussed, the results of received system runs are analyzed, and directions for future research are identified.
View on PubMed (opens in a new tab)

NeuroNER: an easy-to-use program for named-entity recognition based on neural networks

NeuroNER is an easy-to-use named-entity recognition tool based on ANNs that can annotate entities using a graphical web-based user interface (BRAT) and be used to train an ANN, which in turn predict entities’ locations and categories in new texts.
View on ACL (opens in a new tab)

Clinically Accurate Chest X-Ray Report Generation

A domain-aware automatic chest X-ray radiology report generation system which first predicts what topics will be discussed in the report, then conditionally generates sentences corresponding to these topics, and is fine-tuned using reinforcement learning.
View PDF on arXiv (opens in a new tab)

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

This work presents the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams, and implements both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model.
View PDF on arXiv (opens in a new tab)

Automated de-identification of free-text medical records

An automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc, and is sufficiently generalized and can be customized to handle text files of any format is described.
View on Springer (opens in a new tab)
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)