Code produced at Open Healthcare Data Hack 13/14 December 2014.

The Python script preprocesses MIMIC II Physionet dataset. Each patient is represented as one row of CSV file with the following features:

• Survival - OUTPUT VARIABLE (0: survived, or 1: not survived)
• Age (years)
• Gender (0: female, or 1: male)
• Height (cm)
• ICUType (1: Coronary Care Unit, 2: Cardiac Surgery Recovery Unit, 3: Medical ICU, or 4: Surgical ICU)
• Weight (kg)

There are 36 temporal attributes in the dataset for which some measurements are taken during 48 hours:

• Albumin (g/dL)
• ALP [Alkaline phosphatase (IU/L)]
• ALT [Alanine transaminase (IU/L)]
• AST [Aspartate transaminase (IU/L)]
• Bilirubin (mg/dL)
• BUN [Blood urea nitrogen (mg/dL)]
• Cholesterol (mg/dL)
• Creatinine [Serum creatinine (mg/dL)]
• DiasABP [Invasive diastolic arterial blood pressure (mmHg)]
• FiO2 [Fractional inspired O2 (0-1)]
• GCS [Glasgow Coma Score (3-15)]
• Glucose [Serum glucose (mg/dL)]
• HCO3 [Serum bicarbonate (mmol/L)] • HCT [Hematocrit (%)]
• HR [Heart rate (bpm)]
• K [Serum potassium (mEq/L)]
• Lactate (mmol/L)
• Mg [Serum magnesium (mmol/L)]
• MAP [Invasive mean arterial blood pressure (mmHg)]
• MechVent [Mechanical ventilation respiration (0:false, or 1:true)]
• Na [Serum sodium (mEq/L)]
• NIDiasABP [Non-invasive diastolic arterial blood pressure (mmHg)]
• NIMAP [Non-invasive mean arterial blood pressure (mmHg)]
• NISysABP [Non-invasive systolic arterial blood pressure (mmHg)] • PaCO2 [partial pressure of arterial CO2 (mmHg)]
• PaO2 [Partial pressure of arterial O2 (mmHg)]
• pH [Arterial pH (0-14)]
• Platelets (cells/nL)
• RespRate [Respiration rate (bpm)]
• SaO2 [O2 saturation in hemoglobin (%)]
• SysABP [Invasive systolic arterial blood pressure (mmHg)]
• Temp [Temperature (°C)]
• TropI [Troponin-I (μg/L)]
• TropT [Troponin-T (μg/L)]
• Urine [Urine output (mL)]
• WBC [White blood cell count (cells/nL)]

For EACH of these attributes, the following features are derived:

• Mean
• Standard deviation
• Averaged gradient (averaged slopes of the lines connecting each two consecutove measurements)
• Standard deviation of gradients (standard deviation of slopes of the lines connecting each two consecutove measurements)