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orange / Orange / datasets / post-operative.htm

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<title>Postoperative Patient Data Base</title>
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<h1>Info on Postoperative Patient Data Base</h1>
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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)
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