{ "data_id": "34", "name": "postoperative-patient-data", "exact_name": "postoperative-patient-data", "version": 1, "version_label": "1", "description": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\n1. Title: Postoperative Patient Data\n \n 2. Source Information:\n -- Creators: Sharon Summers, School of Nursing, University of Kansas\n Medical Center, Kansas City, KS 66160\n Linda Woolery, School of Nursing, University of Missouri,\n Columbia, MO 65211\n -- Donor: Jerzy W. Grzymala-Busse (jerzy@cs.ukans.edu) (913)864-4488\n -- Date: June 1993\n \n 3. Past Usage:\n 1. A. Budihardjo, J. Grzymala-Busse, L. Woolery (1991). Program LERS_LB 2.5\n as a tool for knowledge acquisition in nursing, Proceedings of the 4th\n Int. Conference on Industrial & Engineering Applications of AI & Expert\n Systems, pp. 735-740.\n \n 2. L. Woolery, J. Grzymala-Busse, S. Summers, A. Budihardjo (1991). The use\n of machine learning program LERS_LB 2.5 in knowledge acquisition for \n expert system development in nursing. Computers in Nursing 9, pp. 227-234.\n \n 4. Relevant Information:\n The classification task of this database is to determine where\n patients in a postoperative recovery area should be sent to next. \n Because hypothermia is a significant concern after surgery\n (Woolery, L. et. al. 1991), the attributes correspond roughly to body \n temperature measurements.\n \n Results:\n -- LERS (LEM2): 48% accuracy\n \n 5. Number of Instances: 90\n \n 6. Number of Attributes: 9 including the decision (class attribute)\n \n 7. Attribute Information:\n 1. L-CORE (patient's internal temperature in C):\n high (> 37), mid (>= 36 and <= 37), low (< 36)\n 2. L-SURF (patient's surface temperature in C):\n high (> 36.5), mid (>= 36.5 and <= 35), low (< 35)\n 3. L-O2 (oxygen saturation in %):\n excellent (>= 98), good (>= 90 and < 98),\n fair (>= 80 and < 90), poor (< 80)\n 4. L-BP (last measurement of blood pressure):\n high (> 130\/90), mid (<= 130\/90 and >= 90\/70), low (< 90\/70)\n 5. SURF-STBL (stability of patient's surface temperature):\n stable, mod-stable, unstable\n 6. CORE-STBL (stability of patient's core temperature)\n stable, mod-stable, unstable\n 7. BP-STBL (stability of patient's blood pressure)\n stable, mod-stable, unstable\n 8. COMFORT (patient's perceived comfort at discharge, measured as\n an integer between 0 and 20)\n 9. decision ADM-DECS (discharge decision):\n I (patient sent to Intensive Care Unit),\n S (patient prepared to go home),\n A (patient sent to general hospital floor)\n \n 8. Missing Attribute Values:\n Attribute 8 has 3 missing values\n \n 9. Class Distribution:\n I (2)\n S (24)\n A (64)\n \n \n \n \n\n Information about the dataset\n CLASSTYPE: nominal\n CLASSINDEX: last", "format": "ARFF", "uploader": "Jan van Rijn", "uploader_id": 1, "visibility": "public", "creator": null, "contributor": null, "date": "2014-04-06 23:22:02", "update_comment": null, "last_update": "2014-04-06 23:22:02", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/34\/dataset_34_postoperative-patient-data.arff", "default_target_attribute": "decision", "row_id_attribute": null, "ignore_attribute": null, "runs": 1758, "suggest": { "input": [ "postoperative-patient-data", "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. C " ], "weight": 5 }, "qualities": { "NumberOfInstances": 90, "NumberOfFeatures": 9, "NumberOfClasses": 3, "NumberOfMissingValues": 3, "NumberOfInstancesWithMissingValues": 3, "NumberOfNumericFeatures": 0, "NumberOfSymbolicFeatures": 9, "RandomTreeDepth1AUC": 0.46185249935249933, "Dimensionality": 0.1, "MaxMutualInformation": 0.05446762725098, "MinNominalAttDistinctValues": 2, "PercentageOfBinaryFeatures": 22.22222222222222, "Quartile2StdDevOfNumericAtts": null, "RandomTreeDepth1ErrRate": 0.45555555555555555, "EquivalentNumberOfAtts": 43.45332433287957, "MaxNominalAttDistinctValues": 4, "MinSkewnessOfNumericAtts": null, "PercentageOfInstancesWithMissingValues": 3.3333333333333335, "Quartile3AttributeEntropy": 1.4097318295258425, "AutoCorrelation": 0.6292134831460674, "RandomTreeDepth1Kappa": -0.10346889952153127, "J48.00001.AUC": 0.5, "MaxSkewnessOfNumericAtts": null, "MinStdDevOfNumericAtts": null, "PercentageOfMissingValues": 0.3703703703703704, "Quartile3KurtosisOfNumericAtts": 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"name": "L-BP", "index": "3", "type": "nominal", "distinct": "3", "missing": "0", "distr": [ [ "high", "low", "mid" ], [ [ "23", "1", "6" ], [ "3", "0", "0" ], [ "38", "1", "18" ] ] ] }, { "name": "SURF-STBL", "index": "4", "type": "nominal", "distinct": "2", "missing": "0", "distr": [ [ "stable", "unstable" ], [ [ "32", "1", "12" ], [ "32", "1", "12" ] ] ] }, { "name": "CORE-STBL", "index": "5", "type": "nominal", "distinct": "3", "missing": "0", "distr": [ [ "mod-stable", "stable", "unstable" ], [ [ "1", "0", "0" ], [ "60", "2", "21" ], [ "3", "0", "3" ] ] ] }, { "name": "BP-STBL", "index": "6", "type": "nominal", "distinct": "3", "missing": "0", "distr": [ [ "mod-stable", "stable", "unstable" ], [ [ "17", "0", "4" ], [ "30", "1", "15" ], [ "17", "1", "5" ] ] ] }, { "name": "COMFORT", "index": "7", "type": "nominal", "distinct": "4", "missing": "3", "distr": [ [ "05", "07", "10", "15" ], [ [ "1", "0", "1" ], [ "0", "0", "1" ], [ "48", "1", "16" ], [ "13", "0", "6" ] ] ] } ], 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