Dandelion Health
The premier multimodal clinical data platform for medical AI training and validation.
The industry-standard multi-center dataset for high-fidelity clinical predictive modeling and ICU informatics.
The eICU Collaborative Research Database (eICU-CRD) represents a landmark achievement in medical informatics, providing a de-identified, multi-center dataset comprising over 200,000 intensive care unit (ICU) admissions across the United States. Developed through a collaboration between the MIT Laboratory for Computational Physiology and Philips Healthcare, the database is sourced from the Philips eICU Program, a telehealth system that supports clinical teams. Technically, the database is structured as a relational schema (PostgreSQL compatible) containing high-resolution longitudinal data, including vital sign measurements, care provider notes, laboratory results, and pharmacy records. In the 2026 market landscape, the eICU-CRD remains the primary benchmark for cross-hospital validation of clinical AI models. Its multi-center nature addresses the 'overfitting' issues common in single-center datasets like MIMIC-IV, making it essential for developing generalized medical LLMs and autonomous diagnostic agents. The architecture supports complex temporal queries and integrates seamlessly with cloud-native analysis tools like Google BigQuery, enabling researchers to execute massive-scale epidemiological studies and real-time clinical decision support simulations without the overhead of local data management.
Aggregates data from 208 distinct hospitals across the US, providing diverse patient demographics and clinical practices.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Includes pre-calculated Acute Physiology and Chronic Health Evaluation (APACHE) scores and components.
Contains time-series vital signs recorded at 5-minute intervals for the duration of the ICU stay.
Standardized medication entries mapped to National Drug Codes (NDC).
Large corpus of free-text nursing notes and physician progress reports with PHI removed.
Data is organized into 31 related tables with clear primary/foreign key relationships (patientID, hospitalID).
Direct availability as a public dataset on GCP for petabyte-scale analytics.
Models trained on one hospital often fail when deployed to another due to 'data shift'.
Registry Updated:2/7/2026
Late detection of sepsis leads to high mortality rates.
ICU burnout due to inefficient staffing.