mimic
$
npx mdskill add mkurman/zorai/mimicQuery curated ICU data to build clinical ML features.
- Enables analysis of vitals, labs, medications, and diagnoses.
- Connects to PostgreSQL hosting MIMIC-III and MIMIC-IV datasets.
- Executes SQL queries to extract time-series and admission data.
- Outputs pandas DataFrames for feature engineering and benchmarking.
SKILL.md
.github/skills/mimicView on GitHub ↗
---
name: mimic
description: "MIMIC (Medical Information Mart for Intensive Care) database toolkit. Curated ICU data: vitals, labs, medications, notes, diagnoses. Tools for querying MIMIC-III/IV, building ML features, and reproducing benchmarks."
tags: [mimic, icu, ehr, clinical-database, research, healthcare, zorai]
---
## Overview
MIMIC (Medical Information Mart for Intensive Care) provides ICU data: vitals, labs, medications, notes, diagnoses. Tools for querying MIMIC-III/IV, building ML features, and reproducing clinical benchmarks.
## Access
Apply for access at https://physionet.org/content/mimiciv/ -- requires CITI data use training.
## Installation
```bash
uv pip install psycopg2 pandas
```
## Python Analysis
```python
import pandas as pd
from sqlalchemy import create_engine
engine = create_engine("postgresql://user:pass@localhost:5432/mimiciv")
# First 24h vitals
query = """
SELECT subject_id, charttime, valuenum
FROM mimiciv_icu.chartevents
WHERE itemid = 220045
AND valuenum IS NOT NULL
LIMIT 100
"""
hr = pd.read_sql(query, engine)
```
## Workflow
1. Apply for MIMIC access (physionet.org)
2. Load MIMIC-IV into PostgreSQL
3. Query ICU stays, diagnoses, labs, medications, notes
4. Extract features (vitals over time, labs at admission, comorbidity scores)
5. Build ML benchmarks (in-hospital mortality, LOS prediction, sepsis detection)