Resources

The MIMIC-IV-ECG module contains approximately 800,000 diagnostic electrocardiograms across nearly 160,000 unique patients. These diagnostic ECGs use 12 leads and are 10 seconds in length. They are sampled at 500 Hz. This subset contains all of the …

Symile-MIMIC is a multimodal clinical dataset derived from MIMIC-IV and MIMIC-CXR, consisting of chest X-rays (CXRs), electrocardiograms (ECGs), and blood laboratory tests. It was developed to evaluate Symile, a contrastive learning objective design…

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier …

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, …

MIMIC-IV-ED is a large, freely available database of emergency department (ED) admissions at the Beth Israel Deaconess Medical Center between 2011 and 2019. The database contains ~425,000 ED stays. Vital signs, triage information, medication reconci…

The MIMIC-IV-ECG Demo module contains 659 diagnostic electrocardiograms across 92 unique patients. These 92 patients overlap with the patients from the MIMIC-IV Clinical Demo and are also part of the MIMIC-IV Clinical Database. These diagnostic ECGs…

The MIMIC-IV Waveform Database is a large collection of physiological signals and measurements from patients in intensive care units, including electrocardiograms, photoplethysmograms, respiration, invasive and non-invasive blood pressure, and more.…

Large language models (LLMs) have shown impressive capabilities in solving a wide range of tasks based on human instructions. However, developing a conversational AI assistant for electronic health record (EHR) data remains challenging due to the la…

Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manne…

With the rapid development of generative LLMs (large language models) in the field of natural language processing, their potential in medical applications has become increasingly evident. However, most existing studies rely on exam-style questions o…