eICU 2.0 Data Extraction Skill#

⚠️ Important Correction (2026-05-20): This document has been thoroughly revised based on the official SchemaSpy documentation and MIT-LCP/eicu-code repository.

Key Corrections:

• ✅ Both vitalperiodic and vitalaperiodic tables exist (I previously incorrectly believed they did not)
• ✅ Field names use camelCase ( heartrate not heart_rate or heartRate)
• ✅ Vital signs data comes from multiple tables ( vitalperiodic, vitalaperiodic, nursecharting)

Extract critical care data from the eICU Collaborative Research Database 2.0. Provides SQL templates and Python code; users handle database connections independently.

Connection Method#

Users provide their own PostgreSQL connection parameters. This skill only provides query code.

Supported Query Types#

Query TypeReference FilePatient Basic Informationreferences/schema.md (patient table)Vital Signs (vitalperiodic)references/vital_signs.mdVital Signs (vitalaperiodic)references/vital_signs.mdVital Signs (nursecharting)references/vital_signs_nursecharting.mdPhysical Examination (GCS, etc.)references/gcs.mdLaboratory Testsreferences/labs.mdBlood Gas Analysisreferences/blood_gas.mdUrine Outputreferences/urine_output.mdVasopressorsreferences/vasopressors.mdInfusionsreferences/infusions.mdWeightreferences/weight.mdOxygenationreferences/oxygenation.mdDiagnoses and APACHE Groupingreferences/diagnoses.mdMedicationsreferences/medications.mdDatabase Schemareferences/schema.mdCommon Query Templatesreferences/common_queries.md

Workflow#

1. Confirm the query type needed by the user
2. Read the corresponding references file to get the SQL/Python template
3. Adjust query conditions based on the user's specific requirements
4. Provide both SQL and Python implementations

Key Differences Between eICU and MIMIC-IV#

FeatureeICU 2.0MIMIC-IVTime RepresentationOffsets (minutes)Absolute timestampsVital Signs Sourcevitalperiodic + vitalaperiodic + nursecharting``chartevents table (mapped via d_items)Laboratory Testslab table ( labname is the name directly)labevents table (mapped via d_labitems)Patient IDpatientunitstayid``stay_idData Volume200,000 patients50,000 patientsField NamingCamelCase (e.g., heartrate)All lowercase, snake_case

Core Table Structures (Correct)#

1. Patient Basic Information - patient Table#

sql

SELECT
    patientunitstayid  -- ICU stay unique identifier
  , uniquepid           -- Patient unique identifier
  , patienthealthsystemstayid  -- Hospital stay identifier
  , hospitalid          -- Hospital ID
  , unitvisitnumber     -- ICU admission count
  , unittype            -- ICU type
  , apacheadmissiondx   -- APACHE IV admission diagnosis (important!)
  , age                 -- Age (censored: >89 displayed as 91)
  , gender              -- Gender ('Male'/'Female')
  , ethnicity           -- Ethnicity
  , hospitaladmitoffset  -- Hospital admission offset (minutes)
  , hospitaldischargeoffset  -- Hospital discharge offset (minutes)
  , unitdischargeoffset  -- ICU discharge offset (minutes)
  , hospitaldischargestatus  -- Hospital discharge status ('Alive'/'Expired')
  , admissionheight     -- Admission height (cm)
  , admissionweight     -- Admission weight (kg)
  , dischargeweight     -- Discharge weight (kg)
FROM patient;

Calculating ICU Length of Stay (hours):

sql

ROUND(unitdischargeoffset/60) AS icu_los_hours

2. Periodic Vital Signs - vitalperiodic Table#

Table Name: vitalperiodic

Record Count: 146,671,642 rows

Description: Periodic vital sign measurements, typically recorded every 1-5 minutes.

sql

SELECT
    vitalperiodicid     -- Record ID (PK)
  , patientunitstayid  -- Patient ID (FK)
  , observationoffset   -- Observation offset (minutes, relative to ICU admission)
  , temperature         -- Temperature (°C)
  , sao2               -- Oxygen saturation (%)
  , heartrate          -- Heart rate (bpm)
  , respiration        -- Respiratory rate (breaths/min)
  , cvp                -- Central venous pressure (mmHg)
  , etco2              -- End-tidal CO2 (mmHg)
  , systemicsystolic   -- Invasive systolic blood pressure (mmHg)
  , systemicdiastolic  -- Invasive diastolic blood pressure (mmHg)
  , systemicmean       -- Invasive mean arterial pressure (mmHg)
  , pasystolic         -- Pulmonary artery systolic pressure (mmHg)
  , padiastolic        -- Pulmonary artery diastolic pressure (mmHg)
  , pamean             -- Pulmonary artery mean pressure (mmHg)
  , st1                -- ST segment lead 1 (mV)
  , st2                -- ST segment lead 2 (mV)
  , st3                -- ST segment lead 3 (mV)
  , icp                -- Intracranial pressure (mmHg)
FROM vitalperiodic
WHERE observationoffset >= 0 AND observationoffset < 1440  -- First 24 hours after admission
  AND patientunitstayid = %(patient_id)s
ORDER BY observationoffset;

Notes:

• All blood pressure values are invasive measurements (arterial line)
• observationoffset is minutes relative to ICU admission time
• Add plausibility checks: heartrate BETWEEN 25 AND 225

---

3. Aperiodic Vital Signs - vitalaperiodic Table#

Table Name: vitalaperiodic

Record Count: 25,075,074 rows

Description: Aperiodic vital sign measurements (typically invasive monitoring data).

sql

SELECT
    vitalaperiodicid   -- Record ID (PK)
  , patientunitstayid   -- Patient ID (FK)
  , observationoffset    -- Observation offset (minutes)
  , noninvasivesystolic  -- Non-invasive systolic blood pressure (mmHg)
  , noninvasivediastolic -- Non-invasive diastolic blood pressure (mmHg)
  , noninvasivemean     -- Non-invasive mean arterial pressure (mmHg)
  , paop               -- Pulmonary artery occlusion pressure (mmHg)
  , cardiacoutput       -- Cardiac output (L/min)
  , cardiacinput        -- Cardiac index (L/min/m²)
  , svr                -- Systemic vascular resistance (dyn·s·cm⁻⁵)
  , svri               -- Systemic vascular resistance index (dyn·s·cm⁻⁵/m²)
  , pvr                -- Pulmonary vascular resistance (dyn·s·cm⁻⁵)
  , pvri               -- Pulmonary vascular resistance index (dyn·s·cm⁻⁵/m²)
FROM vitalaperiodic
WHERE observationoffset >= 0 AND observationoffset < 1440  -- First 24 hours after admission
  AND patientunitstayid = %(patient_id)s
ORDER BY observationoffset;

Notes:

• This table primarily contains invasive hemodynamic monitoring data
• paop (Pulmonary Artery Occlusion Pressure) is also known as PCWP (Pulmonary Capillary Wedge Pressure)
• svr/ svri and pvr/ pvri are used to assess vascular resistance

---

4. Vital Signs from Nursing Charts - nursecharting Table#

Table Name: nursecharting

Record Count: 151,604,232 rows

Description: Nursing chart records (most detailed nursing data, including vital signs, scores, etc.).

sql

SELECT
    nursechartid              -- Record ID (PK)
  , patientunitstayid        -- Patient ID (FK)
  , nursingchartoffset       -- Charting offset time (minutes)
  , nursingchartentryoffset  -- Entry offset time (minutes)
  , nursingchartcelltypecat  -- Major category (e.g., 'Vital Signs')
  , nursingchartcelltypevallabel  -- Middle category (e.g., 'Heart Rate', 'Non-Invasive BP')
  , nursingchartcelltypevalname   -- Minor category (e.g., 'Heart Rate', 'Non-Invasive BP Systolic')
  , nursingchartvalue        -- Value (text or numeric)
FROM nursecharting
WHERE nursingchartcelltypecat IN ('Vital Signs', 'Scores', 'Other Vital Signs and Infusions')
  AND patientunitstayid = %(patient_id)s
  AND nursingchartoffset >= 0 AND nursingchartoffset < 1440  -- First 24 hours after admission
ORDER BY nursingchartoffset;

Common Patterns for Extracting Vital Signs (learned from eicu-code):

sql

SELECT
    patientunitstayid
  , nursingchartoffset
  , AVG(CASE WHEN nursingchartcelltypevallabel = 'Heart Rate'
               AND nursingchartcelltypevalname = 'Heart Rate'
               AND nursingchartvalue ~ '^[-]?[0-9]+[.]?[0-9]*$'
               AND nursingchartvalue NOT IN ('-','.')
              THEN CAST(nursingchartvalue AS NUMERIC)
              ELSE NULL END) AS heartrate
  , AVG(CASE WHEN nursingchartcelltypevallabel = 'Non-Invasive BP'
               AND nursingchartcelltypevalname = 'Non-Invasive BP Systolic'
               -- similar handling for other vital signs...
              THEN CAST(nursingchartvalue AS NUMERIC)
              ELSE NULL END) AS nibp_systolic
FROM nursecharting
WHERE nursingchartcelltypecat IN ('Vital Signs', 'Scores', 'Other Vital Signs and Infusions')
  AND patientunitstayid = %(patient_id)s
  AND nursingchartoffset >= 0 AND nursingchartoffset < 1440
GROUP BY patientunitstayid, nursingchartoffset
ORDER BY patientunitstayid, nursingchartoffset;

---

5. Laboratory Tests - lab Table#

Table Name: lab

Record Count: 39,132,531 rows

Description: Laboratory test results. labname is the test name directly ( no itemid mapping needed).

sql

SELECT
    labid                   -- Record ID (PK)
  , patientunitstayid       -- Patient ID (FK)
  , labresultoffset         -- Result offset time (minutes)
  , labresultrevisedoffset  -- Result revision offset time (minutes)
  , labname                 -- Test name (string)
  , labresult               -- Test result (numeric)
FROM lab
WHERE labname IN (
      'albumin'
    , 'total bilirubin'
    , 'BUN'
    , 'calcium'
    , 'chloride'
    , 'creatinine'
    , 'bedside glucose', 'glucose'
    , 'bicarbonate'  -- HCO3
    , 'Total CO2'
    , 'Hct'
    , 'Hgb'
    , 'PT - INR'
    , 'PTT'
    , 'lactate'
    , 'platelets x 1000'
    , 'potassium'
    , 'sodium'
    , 'WBC x 1000'
    , '-bands'
    -- Liver enzymes
    , 'ALT (SGPT)'
    , 'AST (SGOT)'
    , 'alkaline phos.'
  )
  AND labresult IS NOT NULL
  AND patientunitstayid = %(patient_id)s
  AND labresultoffset >= 0 AND labresultoffset < 1440  -- First 24 hours after admission
  -- Add plausibility checks
  AND (
        (labname = 'albumin' AND labresult >= 0.5 AND labresult <= 6.5)
    OR (labname = 'creatinine' AND labresult >= 0.1 AND labresult <= 28.28)
    -- ... plausibility checks for other tests
  )
ORDER BY labresultoffset;

Common labname Values:

• Albumin: 'albumin'
• Total bilirubin: 'total bilirubin'
• Blood urea nitrogen: 'BUN'
• Calcium: 'calcium'
• Chloride: 'chloride'
• Creatinine: 'creatinine'
• Blood glucose: 'bedside glucose' or 'glucose'
• Bicarbonate: 'bicarbonate' or 'Total CO2'
• Hematocrit: 'Hct'
• Hemoglobin: 'Hgb'
• Prothrombin time INR: 'PT - INR'
• Partial thromboplastin time: 'PTT'
• Lactate: 'lactate'
• Platelets: 'platelets x 1000'
• Potassium: 'potassium'
• Sodium: 'sodium'
• White blood cells: 'WBC x 1000'
• Bands: '-bands'
• Alanine aminotransferase: 'ALT (SGPT)'
• Aspartate aminotransferase: 'AST (SGOT)'
• Alkaline phosphatase: 'alkaline phos.'

---

6. Diagnoses - patient Table (APACHE Diagnoses)#

Diagnosis information in eICU primarily comes from the apacheadmissiondx field in the patient table (APACHE IV admission diagnosis).

sql

SELECT
    patientunitstayid
  , apacheadmissiondx
  , age
  , gender
  , hospitaldischargestatus
  , ROUND(unitdischargeoffset/60) AS icu_los_hours
FROM patient
ORDER BY patientunitstayid;

APACHE Diagnosis Grouping (learned from eicu-code):

eicu-code provides diagnosis/apache-groups.sql, which groups apacheadmissiondx into:

• 'ACS' (Acute Coronary Syndrome)
• 'ChestPainUnknown' (Chest pain of unknown etiology)
• 'CHF' (Congestive Heart Failure)
• 'CardiacArrest' (Cardiac Arrest)
• 'CABG' (Coronary Artery Bypass Graft)
• 'ValveDz' (Valvular Heart Disease)
• 'PNA' (Pneumonia)
• 'RespMedOther' (Other Respiratory Diseases)
• 'Asthma-Emphys' (Asthma/Emphysema)
• 'GIBleed' (Gastrointestinal Bleeding)
• 'GIObstruction' (Intestinal Obstruction)
• 'CVA' (Cerebrovascular Accident)
• 'Neuro' (Neurological Diseases)
• 'Coma' (Coma)
• 'Overdose' (Drug Overdose)
• 'Sepsis' (Sepsis)
• 'ARF' (Acute Renal Failure)
• 'DKA' (Diabetic Ketoacidosis)
• 'Trauma' (Trauma)
• 'Other' (Other)

---

Time Representation (Important!)#

eICU uses offsets (minutes) to represent time, with all offsets relative to ICU admission time:

• offset = 0: ICU admission moment
• offset = 60: 60 minutes after admission (1 hour)
• offset = 1440: 1440 minutes after admission (24 hours)
• Negative values: Records before admission (if any)

Key Time Fields:

• hospitaladmitoffset - Hospital admission offset
• hospitaldischargeoffset - Hospital discharge offset
• unitdischargeoffset - ICU discharge offset ( used for calculating ICU LOS)
• observationoffset - Vital signs record offset time
• nursingchartoffset - Nursing chart offset time
• labresultoffset - Laboratory result offset time
• diagnosisoffset - Diagnosis offset time

Calculating ICU Length of Stay (hours):

sql

ROUND(unitdischargeoffset/60) AS icu_los_hours

---

Data Quality and Performance Optimization#

Data Quality#

• High Missing Rates: Data completeness in eICU is lower than in MIMIC-IV
• Outliers: Always add plausibility checks in queries (e.g., heartrate BETWEEN 25 AND 225)
• Text Values: Some vital sign values are text (e.g., '80s' means 'in their 80s'), requiring cleaning

Performance Optimization#

Large Tables (from eicu-code statistics):

• nursecharting: ~151 million rows
• lab: ~39.13 million rows
• vitalperiodic: ~146.67 million rows
• vitalaperiodic: ~25.07 million rows
• patient: ~200,000 rows

Optimization Tips:

1. Filter by patientunitstayid (most important!)
2. Limit time range ( observationoffset BETWEEN 0 AND 1440)
3. Add LIMIT clause (during testing)
4. Use EXPLAIN to analyze query plans
5. Consider creating materialized views ( CREATE MATERIALIZED VIEW)

---

Common Query Scenarios#

1. Extract Patient Basic Information#

sql

SELECT
    pt.patientunitstayid
  , pt.age
  , pt.apacheadmissiondx
  , CASE WHEN pt.gender = 'Male' THEN 1
         WHEN pt.gender = 'Female' THEN 2
         ELSE NULL END AS gender
  , CASE WHEN pt.hospitaldischargestatus = 'Alive' THEN 0
         WHEN pt.hospitaldischargestatus = 'Expired' THEN 1
         ELSE NULL END AS hosp_mortality
  , ROUND(pt.unitdischargeoffset/60) AS icu_los_hours
FROM eicu_crd.patient pt
ORDER BY pt.patientunitstayid;

2. Extract ICU Stay Details (Materialized View)#

sql

DROP MATERIALIZED VIEW IF EXISTS icustay_detail CASCADE;
CREATE MATERIALIZED VIEW icustay_detail AS (
  SELECT
      pt.uniquepid
    , pt.patienthealthsystemstayid
    , pt.patientunitstayid
    , pt.unitvisitnumber
    , pt.hospitalid
    , h.region
    , pt.unittype
    , pt.hospitaladmitoffset
    , pt.hospitaldischargeoffset
    , 0 AS unitadmitoffset
    , pt.unitdischargeoffset
    , ap.apachescore AS apache_iv
    , pt.hospitaldischargeyear
    , pt.age
    , CASE WHEN LOWER(pt.hospitaldischargestatus) LIKE '%alive%' THEN 0
           WHEN LOWER(pt.hospitaldischargestatus) LIKE '%expired%' THEN 1
           ELSE NULL END AS hosp_mort
    , CASE WHEN LOWER(pt.gender) LIKE '%female%' THEN 0
           WHEN LOWER(pt.gender) LIKE '%male%' THEN 1
           ELSE NULL END AS gender
    , pt.ethnicity
    , pt.admissionheight
    , pt.admissionweight
    , pt.dischargeweight
    , ROUND(pt.unitdischargeoffset/60) AS icu_los_hours
  FROM patient pt
  LEFT JOIN hospital h
    ON pt.hospitalid = h.hospitalid
  LEFT JOIN apachepatientresult ap
    ON pt.patientunitstayid = ap.patientunitstayid
    AND ap.apacheversion = 'IV'
  ORDER BY pt.uniquepid, pt.unitvisitnumber, pt.age
);

3. Extract Vital Signs (Pivot Format)#

sql

DROP TABLE IF EXISTS pivoted_vital CASCADE;
CREATE TABLE pivoted_vital AS
WITH nc AS (
  SELECT
      patientunitstayid
    , nursingchartoffset
    , nursingchartentryoffset
    , CASE
        WHEN nursingchartcelltypevallabel = 'Heart Rate'
         AND nursingchartcelltypevalname = 'Heart Rate'
         AND nursingchartvalue ~ '^[-]?[0-9]+[.]?[0-9]*$'
         AND nursingchartvalue NOT IN ('-','.')
            THEN CAST(nursingchartvalue AS NUMERIC)
        ELSE NULL END
      AS heartrate
    , CASE
        WHEN nursingchartcelltypevallabel = 'Non-Invasive BP'
         AND nursingchartcelltypevalname = 'Non-Invasive BP Systolic'
         AND nursingchartvalue ~ '^[-]?[0-9]+[.]?[0-9]*$'
         AND nursingchartvalue NOT IN ('-','.')
            THEN CAST(nursingchartvalue AS NUMERIC)
        ELSE NULL END
      AS nibp_systolic
    -- similar handling for other vital signs...
  FROM nursecharting
  WHERE nursingchartcelltypecat IN ('Vital Signs', 'Scores', 'Other Vital Signs and Infusions')
)
SELECT
    patientunitstayid
  , nursingchartoffset AS chartoffset
  , nursingchartentryoffset AS entryoffset
  , AVG(CASE WHEN heartrate BETWEEN 25 AND 225 THEN heartrate ELSE NULL END) AS heartrate
  , AVG(CASE WHEN nibp_systolic BETWEEN 25 AND 250 THEN nibp_systolic ELSE NULL END) AS nibp_systolic
  -- ... other vital signs
FROM nc
WHERE heartrate IS NOT NULL
   OR nibp_systolic IS NOT NULL
   -- ...
GROUP BY patientunitstayid, nursingchartoffset, nursingchartentryoffset
ORDER BY patientunitstayid, nursingchartoffset, nursingchartentryoffset;

4. Extract Laboratory Tests (Pivot Format)#

sql

DROP TABLE IF EXISTS pivoted_lab CASCADE;
CREATE TABLE pivoted_lab AS
WITH vw0 AS (
  SELECT
      patientunitstayid
    , labname
    , labresultoffset
    , labresultrevisedoffset
  FROM lab
  WHERE labname IN (
      'albumin', 'total bilirubin', 'BUN', 'calcium', 'chloride',
      'creatinine', 'bedside glucose', 'glucose', 'bicarbonate', 'Total CO2',
      'Hct', 'Hgb', 'PT - INR', 'PTT', 'lactate',
      'platelets x 1000', 'potassium', 'sodium', 'WBC x 1000', '-bands',
      'ALT (SGPT)', 'AST (SGOT)', 'alkaline phos.'
    )
  GROUP BY patientunitstayid, labname, labresultoffset, labresultrevisedoffset
  HAVING COUNT(DISTINCT labresult) <= 1
),
-- Get the last revised result
vw1 AS (
  SELECT
      lab.patientunitstayid
    , lab.labname
    , lab.labresultoffset
    , lab.labresultrevisedoffset
    , lab.labresult
    , ROW_NUMBER() OVER (
        PARTITION BY lab.patientunitstayid, lab.labname, lab.labresultoffset
        ORDER BY lab.labresultrevisedoffset DESC
      ) AS rn
  FROM lab
  INNER JOIN vw0
    ON  lab.patientunitstayid = vw0.patientunitstayid
    AND lab.labname = vw0.labname
    AND lab.labresultoffset = vw0.labresultoffset
    AND lab.labresultrevisedoffset = vw0.labresultrevisedoffset
  WHERE
      (lab.labname = 'albumin' AND lab.labresult >= 0.5 AND lab.labresult <= 6.5)
    OR (lab.labname = 'creatinine' AND lab.labresult >= 0.1 AND lab.labresult <= 28.28)
    -- ... plausibility checks for other tests
)
SELECT
    patientunitstayid
  , labresultoffset AS chartoffset
  , MAX(CASE WHEN labname = 'albumin' THEN labresult ELSE NULL END) AS albumin
  , MAX(CASE WHEN labname = 'total bilirubin' THEN labresult ELSE NULL END) AS bilirubin
  , MAX(CASE WHEN labname = 'BUN' THEN labresult ELSE NULL END) AS bun
  , MAX(CASE WHEN labname = 'creatinine' THEN labresult ELSE NULL END) AS creatinine
  -- ... other tests
FROM vw1
WHERE rn = 1
GROUP BY patientunitstayid, labresultoffset
ORDER BY patientunitstayid, labresultoffset;

---

Python Code Examples#

Using psycopg2 to Query eICU Data#

python

import psycopg2
import pandas as pd

# Database connection parameters
conn_params = {
    'host': 'localhost',
    'port': 5432,
    'database': 'eicu',
    'user': 'your_username',
    'password': 'your_password'
}

# Query vital signs data
def get_vital_signs(patient_id, hour_window=24):
    """
    Extract vital signs data for a specified patient in the first N hours after ICU admission.

    Args:
        patient_id: ICU stay ID (patientunitstayid)
        hour_window: Time window in hours, default 24 hours
    """
    query = """
        SELECT
            vp.patientunitstayid,
            vp.observationoffset,
            vp.temperature,
            vp.sao2,
            vp.heartrate,
            vp.respiration,
            vp.systemicsystolic,
            vp.systemicdiastolic,
            vp.systemicmean
        FROM vitalperiodic vp
        WHERE vp.patientunitstayid = %(patient_id)s
          AND vp.observationoffset >= 0
          AND vp.observationoffset < %(max_offset)s
        ORDER BY vp.observationoffset;
    """

    # Convert hours to minutes
    max_offset = hour_window * 60

    with psycopg2.connect(**conn_params) as conn:
        df = pd.read_sql(
            query,
            conn,
            params={'patient_id': patient_id, 'max_offset': max_offset}
        )

    return df

# Query laboratory test data
def get_lab_results(patient_id, hour_window=24):
    """
    Extract laboratory test data for a specified patient in the first N hours after ICU admission.
    """
    query = """
        SELECT
            l.patientunitstayid,
            l.labresultoffset,
            l.labname,
            l.labresult
        FROM lab l
        WHERE l.patientunitstayid = %(patient_id)s
          AND l.labresultoffset >= 0
          AND l.labresultoffset < %(max_offset)s
          AND l.labname IN (
              'creatinine', 'BUN', 'sodium', 'potassium',
              'chloride', 'bicarbonate', 'calcium'
          )
          AND l.labresult IS NOT NULL
        ORDER BY l.labresultoffset;
    """

    max_offset = hour_window * 60

    with psycopg2.connect(**conn_params) as conn:
        df = pd.read_sql(
            query,
            conn,
            params={'patient_id': patient_id, 'max_offset': max_offset}
        )

    return df

# Usage example
if __name__ == '__main__':
    # Replace with actual patientunitstayid
    patient_id = 123456

    # Get vital signs
    vitals_df = get_vital_signs(patient_id, hour_window=24)
    print(f"Got {len(vitals_df)} vital signs records")
    print(vitals_df.head())

    # Get laboratory tests
    labs_df = get_lab_results(patient_id, hour_window=24)
    print(f"\nGot {len(labs_df)} lab results records")
    print(labs_df.head())

---

Reference Links#

• Official SchemaSpy Documentation: https://lcp.mit.edu/eicu-schema-spy/index.html
• eICU Data Introduction: https://eicu-crd.mit.edu/
• Access Application: https://physionet.org/content/eicu-crd/2.0/
• eICU-code Official Repository: https://github.com/MIT-LCP/eicu-code
• MIMIC-code Official Repository: https://github.com/MIT-LCP/mimic-code

---

Last Updated: 2026-05-20

Updated By: Wukong (thoroughly revised based on official SchemaSpy documentation and MIT-LCP/eicu-code repository)