Journal of Healthcare Engineering / 2018 / Article / Tab 2

Research Article

An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes

Table 2

Summary of categorical predictor variables (abnormal ranges indicated in bold).

PredictorCategoriesRanges/categories

Age818–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, >90
Gender2Male, female
Arrival mode2Via ambulance, walk in
Temperature (°F)6<94.8, 94.8–96.1, 96.1–99.2, 99.2–100.4, >100.4
Pulse (bpm)8<49, 49–59, 59–105, 105–109, 109–119, 119–129, >129
Respiratory rate (bpm)6<13, 13-14, 14–19, 19–23, >23
Blood pressure (mmHG)6<99, 99–106, 106–176, 176–199, >199
Oxygen saturation (%)4<93, 93-94, >94

Each vital sign also includes an additional category for missing data.