| 
| 1. Title: Postoperative Patient Data
| 
| 2. Source Information:
|    -- Creators: Sharon Summers, School of Nursing, University of Kansas
|                 Medical Center, Kansas City, KS 66160
|                 Linda Woolery, School of Nursing, University of Missouri,
|                 Columbia, MO 65211
|    -- Donor:    Jerzy W. Grzymala-Busse (jerzy@cs.ukans.edu) (913)864-4488
|    -- Date:     June 1993
| 
| 3. Past Usage:
|    1. A. Budihardjo, J. Grzymala-Busse, L. Woolery (1991). Program LERS_LB 2.5
|       as a tool for knowledge acquisition in nursing, Proceedings of the 4th
|       Int. Conference on Industrial & Engineering Applications of AI & Expert
|       Systems, pp. 735-740.
| 
|    2. L. Woolery, J. Grzymala-Busse, S. Summers, A. Budihardjo (1991). The use
|       of machine learning program LERS_LB 2.5 in knowledge acquisition for 
|       expert system development in nursing. Computers in Nursing 9, pp. 227-234.
| 
| 4. Relevant Information:
|       The classification task of this database is to determine where
|       patients in a postoperative recovery area should be sent to next.  
|       Because hypothermia is a significant concern after surgery
|       (Woolery, L. et. al. 1991), the attributes correspond roughly to body 
|       temperature measurements.
| 
|       Results:
|       -- LERS (LEM2): 48% accuracy
| 
| 5. Number of Instances: 90
| 
| 6. Number of Attributes: 9 including the decision (class attribute)
| 
| 7. Attribute Information:
|      1. L-CORE (patient's internal temperature in C):
|               high (> 37), mid (>= 36 and <= 37), low (< 36)
|      2. L-SURF (patient's surface temperature in C):
|               high (> 36.5), mid (>= 36.5 and <= 35), low (< 35)
|      3. L-O2 (oxygen saturation in %):
|               excellent (>= 98), good (>= 90 and < 98),
|               fair (>= 80 and < 90), poor (< 80)
|      4. L-BP (last measurement of blood pressure):
|               high (> 130/90), mid (<= 130/90 and >= 90/70), low (< 90/70)
|      5. SURF-STBL (stability of patient's surface temperature):
|               stable, mod-stable, unstable
|      6. CORE-STBL (stability of patient's core temperature)
|               stable, mod-stable, unstable
|      7. BP-STBL (stability of patient's blood pressure)
|               stable, mod-stable, unstable
|      8. COMFORT (patient's perceived comfort at discharge, measured as
|               an integer between 0 and 20)
|      9. decision ADM-DECS (discharge decision):
|               I (patient sent to Intensive Care Unit),
|               S (patient prepared to go home),
|               A (patient sent to general hospital floor)
| 
| 8. Missing Attribute Values:
|      Attribute 8 has 3 missing values
| 
| 9. Class Distribution:
|      I (2)
|      S (24)
|      A (64)

I, S, A

Internal temperature: low, mid, high.
Surface temperature: low, mid, high.
Oxygen saturation:   poor, fair, good, excellent.
Blood pressure: low, mid, high.
Surface temperature stability: stable, mod-stable, unstable.
Internal temperature stability: stable, mod-stable, unstable.
Blood pressure stability: stable, mod-stable, unstable.
Comfort: continuous.