Critical Care

The eICU Collaborative Research Database (eICU-CRD) is a large multi-center intensive care unit database from Philips Healthcare's eICU telehealth program, in partnership with MIT Laboratory for Computational Physiology. Contains de-identified data for over 200,000 admissions from 139,000 unique patients across 335 ICU units at 208 US hospitals (2014-2015). Includes: demographics, vital signs, care plan documentation, severity of illness measures (APACHE IV), diagnoses (3,933 unique active problems), laboratory measurements (158 lab types), medications, continuous infusions, intake/output, microbiology, nurse charting, and structured notes. Data access follows the same process as MIMIC: free of charge, requires PhysioNet credentialed access (CITI Data or Specimens Only Research course + Data Use Agreement). Complements MIMIC-IV (single-center, Boston) with multi-center US coverage for external validation and generalizability of ICU ML models.

eICU, critical care, ICU, multicenter, Philips, PhysioNet, vital signs, severity of illness

Critical care patients are monitored closely through the course of their illness. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU) database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States.

MIMIC-IV is a large, freely-available de-identified clinical database comprising over 300,000 patients admitted to the Beth Israel Deaconess Medical Center (2008-2019). It includes comprehensive ICU and Emergency Department data: demographics, vital signs, laboratory measurements, medications, procedures, diagnoses (ICD codes), imaging reports, nursing notes, and mortality outcomes. The relational database (BigQuery or local PostgreSQL) links hospital admissions (ADMISSIONS), patient stays (ICUSTAYS), charted observations (CHARTEVENTS), lab events (LABEVENTS), microbiology data (MICROBIOLOGYEVENTS), prescriptions (PRESCRIPTIONS), and discharge summaries. MIMIC-IV replaces MIMIC-III (2001-2012) with a modernized schema, cleaner data model, and expanded coverage. Widely used for developing and benchmarking clinical AI models (mortality prediction, sepsis detection, phenotyping, NLP), it requires credentialed access via PhysioNet (CITI Data or Specimens Only course). Supporting datasets include MIMIC-CXR (chest X-ray images) and MIMIC-NOTE (de-identified clinical notes).

EHR, ICU, clinical database, de-identified, Beth Israel, critical care, MIMIC, medical informatics

MIMIC-IV is a publicly available database of de-identified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts. The database is updated annually and is freely available to credentialed researchers. MIMIC-IV contains information on patient demographic characteristics, vital signs, laboratory measurements, medications, and diagnoses. We describe the process of creating the database, the structure of the data, and the tools available to users. MIMIC-IV is a valuable resource for researchers in critical care, clinical informatics, and machine learning.