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术后患者数据集

术后患者数据集

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Medical Classification

Data Set Information:The classification task of this database is to determine where patients in a postoperative rec......

数据结构 ? 2K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Data Set 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


    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 (


    Relevant Papers:

    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.
    [Web link]

    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.


    Papers That Cite This Data Set1:


    Petri Kontkanen and Jussi Lahtinen and Petri Myllym?ki and Henry Tirri. Unsupervised Bayesian visualization of high-dimensional data. KDD. 2000.  [View Context].

    Art B. Owen. Tubular neighbors for regression and classification. Stanford University. 1999.  [View Context].

    Glenn Fung and Sathyakama Sandilya and R. Bharat Rao. Rule extraction from Linear Support Vector Machines. Computer-Aided Diagnosis & Therapy, Siemens Medical Solutions, Inc.  [View Context].

    Citation Request:

    Please refer to the Machine Learning Repository's citation policy


    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

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