Acute Hypotension

This dataset comprises vital signs, lab tests, administered fluid boluses and vasopressors for 3,910 patients with acute hypotension in the intensive care unit.

You can access our synthetic sepsis dataset through PhysioNet.

What you should know about this dataset

These synthetic data were generated based on the MIMIC-III Clinical Database. The cohort was defined according to [1]Gottesman O, Futoma J, Liu Y, Parbhoo S, Celi L, Brunskill E, Doshi-Velez F. Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. Proceedings of the … Continue reading, and included adults with an initial ICU stay of at least 24 hours and seven or more mean arterial pressure (MAP) values of 65mmHg or less, indicating probable acute hypotension. All synthetic records have a length of 48 hours.


The following tables contain a list of variables in the synthetic data together with descriptive statistics.

wdt_ID Variable name Data Type Unit Numeric Statistics
1 Mean Arterial Pressure (MAP) numeric mmHg Median: 65.34 (Q1: 59.30, Q3: 71.19)
2 Diastolic Blood Pressure (BP) numeric mmHg Median: 54.33 (Q1: 48.37, Q3: 60.26)
3 Systolic BP numeric mmHg Median: 113.21 (Q1: 104.23, Q3: 121.60)
4 Urine numeric mL Median: 106.21 (Q1: 68.92, Q3: 164.23)
5 Alanine Transaminase (ALT) numeric IU/L Median: 32.55 (Q1: 24.59, Q3: 46.09)
6 Aspartate Aminotransferase (AST) numeric IU/L Median: 46.82 (Q1: 35.81, Q3: 67.75)
7 Partial Pressure of Oxygen (PO2) numeric mmHg Median: 103.02 (Q1: 91.34, Q3: 114.66)
8 Lactic Acid numeric mmol/L Median: 1.50 (Q1: 1.29, Q3: 1.80)
9 Serum Creatinine numeric mg/dL Median: 1.11 (Q1: 0.83, Q3: 1.62)
10 Fluid Boluses categorical mL 4 Classes (0, 250): 97.32%; (250, 500): 0.28%; (500, 1000) : 1.46% ≥ 1000 : 0.94%
11 Vasopressors categorical mcg/kg/min 4 Classes 0: 84.14%; (0, 8.4): 8.34%; (8.4, 20.28): 3.68%; ≥ 20.28: 3.83%
12 Fraction of Inspired Oxygen (FiO2) categorical - 10 Classes ≤ 0.2: 0.00%; 0.2: 0.54%; 0.3: 2.84%; 0.4: 10.85%; 0.5: 63.30%; 0.6: 8.58%; 0.7: 1.32%; 0.8: 0.20%; 0.9: 2.63%; 1.0: 9.75%
13 Glasgow Coma Scale (GCS) categorical - 13 Classes 3: 6.61%; 4: 2.16%; 5: 0.00%; 6: 3.00%; 7: 4.77%; 8: 0.00%; 9: 2.22%; 10: 4.32%; 11: 2.46%; 12: 3.56%; 13: 1.00%; 14: 9.80%; 15: 60.09%
14 Urine Data Missing (Urine (M)) binary True/False False: 63.07%, True: 36.93%
15 FiO2 Data Missing (FiO2 (M)) binary True/False False: 98.50%, True: 1.50%
16 ALT or AST Data Missing (ALT/AST (M)) binary True/False False: 92.49%, True: 7.51%
17 GCS Data Missing (CGS (M)) binary True/False False: 81.49%, True: 18.51%
18 PO2 Data Missing (PO2 (M)) binary True/False False: 97.56%, True: 2.44%
19 Lactic Acid Data Missing (PO2 (M)) binary True/False False: 96.98%, True: 3.02%
20 Serum Creatinine Data Missing (Serum Creatinine (M)) binary True/False False: 95.26%, True: 4.74%

Comparison of marginal distributions

Comparison of marginal distributions between the real and synthetic data for continuous, categorical and binary variables.

Numeric variable comparisons

E.g. MAP, BP, Systolic BP, Urine, ALT, AST, PO2, Lactic Acid, Serum Creatinine

Categorical variable comparisons

E.g. Fluid Boluses, Vasopressors, FiO2, GCS

Binary variable comparisons

E.g. Urine (M), ALT/AST (M), FiO2 (M), CGS (M), PO2 (M), Lactic Acid (M), Serum Creatinine (M)

Comparison of correlations

The matrices below show:

1) Static correlations between pairs of variables for all patients at any time, in the real and synthetic data.

2) Dynamic correlations between variables in the real and synthetic data. Time series for each patient were linearly decomposed into trends (indicating a general upward or downward slope) and cycles (indicating periodic patterns). Correlations were then computed between trends and cycles for all patients.

Real Data


Real Data


Real Data



Health Gym data can be downloaded either directly from this website or using the Python API.

Note: This section is still in progress, an API will be available soon

Install the health gym python package through pip by running the following in your terminal.

					pip install healthgym

To download and access the data from within python.

					import healthgym as hg

hypotension_data = hg.datasets.Hypotension(root: 'path/to/data/', download = True)


All datasets support the following parameters.

					root (string)
# Root directory of dataset where dataset exists or will be saved to if download is set to True. 

download (bool, optional) 
# If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

Download the dataset

Direct download as CSV file or view GitHub repository

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