Resources

Discharge summaries in Electronic Health Records (EHRs) are crucial for clinical decision-making, but their length and complexity make information extraction challenging, especially when dealing with accumulated summaries across multiple patient adm…

Opioid related aberrant behaviors (ORAB) present novel risk factors for opioid overdose. Previously, ORAB have been mainly assessed by survey results and by monitoring drug administrations. Such methods however, cannot scale up and do not cover the …

Automatically summarizing patients’ main problems from the daily care notes in the electronic health record can help mitigate information and cognitive overload for clinicians and provide augmented intelligence via computerized diagnostic decision s…

Pulse oximeters measure peripheral arterial oxygen saturation (SpO2) noninvasively, while the gold standard involves arterial blood gas measurement (SaO2). There are known racial and ethnic disparities in their performance. BOLD is a new comprehensi…

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, …

Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modelling textual features and relation prediction [1] . However, there is a paucity of annotated cor…

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…

This project contains the weights of 7 different BERT models trained over a surrogate re-identified set of MIMIC-III (v1.4) notes. Each model is suitable for direct compatibility with the HuggingFace framework; users can easily load and apply the mo…

AMR-UTI is a freely accessible dataset, derived from electronic health record (EHR) information on over 80,000 patients with urinary tract infections (UTI) treated at Massachusetts General Hospital and Brigham & Women's Hospital in Boston, MA, USA b…

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…