Resources

The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, …

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newl…

Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing chart. These da…

The problem of natural language inference (NLI) determines whether a natural language hypothesis can be justifiably inferred from a natural language premise. NLI has attracted researchers to benchmark it in a number of settings including medical one…

The Paediatric Intensive Care (PIC) database is a large paediatric-specific, single-center, bilingual database comprising information relating to children admitted to critical care units at a large children’s hospital in China. The database is de-id…

The Gout Emergency Department Chief Complaint Corpora (GED3C) consists of 2 corpora of free text triage nurse chief complaints (up to 282 characters in length) collected from 2019 to 2020 at an academic medical center in the Deep South. The smaller …

This dataset contains TensorFlow SequenceExamples derived from patient records in MIMIC-III, a freely available set of deidentified medical records from critical care patients at Beth Israel Deaconess Medical Center. Each SequenceExample converts da…

A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much …

Natural Language Inference (NLI) is the task of determining whether a given hypothesis can be inferred from a given premise. Also known as Recognizing Textual Entailment (RTE), this task has enjoyed popularity among researchers for some time. Howeve…

State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training…