
Test Datasets
This dataset comprises vital signs, lab tests, administered fluid boluses and vasopressors for 3,910 patients with acute hypotension in the intensive care unit.
What you should know about this dataset
- Variables
- Distribution
- Correlation
Variables
Variable Name | Data Type | Unit | Numeric Statistics | ||||
Median | Q1 | Q3 | |||||
Age | numeric | year | 65.40 | 58.29 | 72.95 | ||
Heart Rate (HR) | numeric | bpm | 89.09 | 78.46 | 99.82 | ||
Systolic BP | numeric | mmHg | 123.67 | 114.43 | 133.03 | ||
Mean BP | numeric | mmHg | 81.02 | 75.18 | 86.91 | ||
Diastolic BP | numeric | mmHg | 58.90 | 50.40 | 66.95 | ||
Respiratory Rate (RR) | numeric | bpm | 21.46 | 18.69 | 24.28 | ||
Potassium (K) | numeric | meq/L | 4.12 | 3.78 | 4.45 | ||
Sodium (Na) | numeric | meq/L | 140.01 | 136.59 | 143.57 | ||
Chloride (Cl−) | numeric | meq/L | 105.23 | 102.08 | 108.03 | ||
Calcium (Ca) | numeric | mg/dL | 8.02 | 7.37 | 8.66 | ||
Ionised Ca | numeric | mg/dL | 1.11 | 1.04 | 1.18 | ||
Carbon Dioxide (CO2) | numeric | meq/L | 25.27 | 23.44 | 27.29 | ||
Albumin | numeric | g/dL | 3.01 | 2.68 | 3.32 | ||
Hemoglobin (Hb) | numeric | g/dL | 10.20 | 9.17 | 11.23 | ||
Potential of Hydrogen (pH) | numeric | - - | 7.39 | 7.34 | 7.44 | ||
Arterial Base Excess (BE) | numeric | meq/L | 0.16 | −2.04 | 2.48 | ||
Bicarbonate (HCO3) | numeric | meq/L | 24.38 | 22.63 | 26.13 | ||
FiO2 | numeric | fraction | 0.45 | 0.38 | 0.55 | ||
Glucose | numeric | mg/dL | 134.11 | 108.21 | 167.06 | ||
Blood Urea Nitrogen (BUN) | numeric | mg/dL | 25.38 | 19.89 | 31.92 | ||
Creatinine | numeric | mg/dL | 1.13 | 0.90 | 1.44 | ||
Magnesium (Mg) | numeric | mg/dL | 2.04 | 1.83 | 2.29 | ||
Serum Glutamic Oxaloacetic Transaminase (SGOT) | numeric | u/L | 50.78 | 31.53 | 88.97 | ||
Serum Glutamic Pyruvic Transaminase (SGPT) | numeric | u/L | 39.99 | 26.20 | 65.66 | ||
Total Bilirubin (Total Bili) | numeric | mg/dL | 1.19 | 0.66 | 2.32 | ||
White Blood Cell Count (WBC) | numeric | E9/L | 10.60 | 7.99 | 13.92 | ||
Platelets Count (Platelets) | numeric | E9/L | 184.44 | 141.97 | 239.41 | ||
PaO2 | numeric | mmHg | 109.07 | 84.22 | 139.63 | ||
Partial Pressure of CO2 (PaCO2) | numeric | mmHg | 39.32 | 34.92 | 44.97 | ||
Lactate | numeric | mmol/L | 1.82 | 1.41 | 2.40 | ||
Total Volume of Intravenous Fluids (Input Total) | numeric | mL | 4867.46 | 1887.84 | 11155.76 | ||
Intravenous Fluids of Each 4-Hour Period (Input 4H) | numeric | mL | 58.66 | 13.83 | 229.01 | ||
Maximum Dose of Vasopressors in 4H (Max Vaso) | numeric | mcg/kg/min | 0.0002 | 0.0 | 0.0017 | ||
Total Volume of Urine Output (Output Total) | numeric | mL | 2505.54 | 585.47 | 6733.69 | ||
Urine Output in 4H (Output 4H) | numeric | mL | 159.33 | 44.74 | 361.69 |
Variable Name | Data Type | Unit | Numeric Statistics |
Gender | binary | - | Male: 73.41% True: 26.59% |
Readmission of Patient (Readmission) | binary | - | False: 60.20% True: 39.80% |
Mechanical Ventilation (Mech) | binary | - | False: 56.89% True: 43.11% |
GCS | categorical | point | 13 Classes 3 : 8.71% 4 : 0.38% 5 : 0.50% 6 : 6.30% 7 : 0.74% 8 : 2.27% 9 : 1.52% 10 : 9.31% 11 : 9.12% 12 : 6.31% 13 : 2.53% 14 : 15.45% 15 : 36.85% |
Peripheral Oxygen Saturation (SpO2) | categorical | % | 10 Classes (C) C1: [0.00, 93.83) : 13.38%; C2: [93.83, 95.14) : 8.12%; C3: [95.14, 96.00) : 4.48%; C4: [96.00, 96.70) : 10.64%; C5: [96.70, 97.33) : 12.61%; C6: [97.33, 98.00) : 11.36%; C7: [98.00, 98.60) : 11.52%; C8: [98.60, 99.22) : 11.84%; C9: [99.22, 99.86) : 8.39%; C10: [99.86, 100.0] : 7.66%; |
Temperature (Temp) | categorical | Celcius | 10 Classes (C) C1: [15.11, 35.95) : 7.83%; C2: [35.95, 36.28) : 6.55%; C3: [36.28, 36.50) : 12.87%; C4: [36.50, 36.69) : 16.56%; C5: [36.69, 36.88) : 4.21%; C6: [36.88, 37.06) : 8.21%; C7: [37.06, 37.28) : 7.10%; C8: [37.28, 37.56) : 9.37%; C9: [37.56, 37.93) : 10.96%; C10: [37.93, 40.52] : 16.33%; |
Partial Thromboplastin Time (PTT) | categorical | s | 10 Classes (C) C1: [17.80, 24.53) : 7.69%; C2: [24.53, 26.63) : 6.71%; C3: [26.63, 28.20) : 10.02%; C4: [28.20, 29.60) : 12.44%; C5: [29.60, 31.45) : 5.46%; C6: [31.45, 34.00) : 9.27%; C7: [34.00, 37.10) : 9.99%; C8: [37.10, 42.80) : 11.47%; C9: [42.80, 57.90) : 12.38%; C10: [57.90, 150.00] : 14.58%; |
Prothrombin Time (PT) | categorical | s | 10 Classes (C) C1: [9.90, 12.20) : 7.89%; C2: [12.20, 12.90) : 8.2%; C3: [12.90, 13.30) : 11.02%; C4: [13.30, 13.80) : 9.84%; C5: [13.80, 14.30) : 9.45%; C6: [14.30, 14.90) : 6.59%; C7: [14.90, 15.90) : 10.37%; C8: [15.90, 17.51) : 10.51%; C9: [17.51, 22.00) : 13.27%; C10: [22.00, 146.70] : 12.85%; |
International Normalised Ratio (INR) | categorical | - | 10 Classes (C) C1: [0.00, 1.00) : 0.19%; C2: [1.00, 1.10) : 8.88%; C3: [1.10, 1.20) : 23.35%; C4: [2.21, 17.60] : 0.09% C5: [1.20, 1.30) : 15.64%; C6: [1.30, 1.31) : 10.22%; C7: [1.31, 1.50) : 7.53%; C8: [1.50, 1.70) : 9.71%; C9: [1.70, 2.21) : 10.67%; C10: [2.21, 17.60] : 13.70%; |
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
- Synthetic
- Real
Categorical variable comparisons
E.g. Fluid Boluses, Vasopressors, FiO2, GCS
- Synthetic
- Real
Binary variable comparisons
E.g. Urine (M), ALT/AST (M), FiO2 (M), CGS (M), PO2 (M), Lactic Acid (M), Serum Creatinine (M)
- Synthetic
- Real
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.
- Static
- Trends
- Cycles
Real Data
Synthetic
Real Data
Synthetic
Real Data
Synthetic
Download the dataset
Direct download as CSV file or view GitHub repository
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References[+]
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 37th International Conference on Machine Learning, PMLR. 2020;119:3658-3667. |
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