# Info on Postoperative Patient Data Base

    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)