Post-Operative Patient
Donated on 5/31/1993
Dataset of patient features
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Categorical, Integer
# Instances
90
# Features
8
Dataset Information
Additional 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
Has Missing Values?
Yes
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| L-CORE | Feature | Categorical | patient's internal temperature | C | no |
| L-SURF | Feature | Categorical | patient's surface temperature | C | no |
| L-O2 | Feature | Categorical | oxygen saturation | % | no |
| L-BP | Feature | Categorical | last measurement of blood pressure | no | |
| SURF-STBL | Feature | Categorical | stability of patient's surface temperature | no | |
| CORE-STBL | Feature | Categorical | stability of patient's core temperature | no | |
| BP-STBL | Feature | Categorical | stability of patient's blood pressure | no | |
| COMFORT | Feature | Integer | patient's perceived comfort at discharge, measured between 0 and 20 | yes | |
| ADM-DECS | Target | Categorical | discharge decision: I (patient sent to Intensive Care Unit), S (patient prepared to go home), A (patient sent to general hospital floor) | no |
0 to 9 of 9
Additional Variable 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)
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset post_operative_patient = fetch_ucirepo(id=82) # data (as pandas dataframes) X = post_operative_patient.data.features y = post_operative_patient.data.targets # metadata print(post_operative_patient.metadata) # variable information print(post_operative_patient.variables)
Summers,Sharon and Woolery,Linda. (1993). Post-Operative Patient. UCI Machine Learning Repository. https://doi.org/10.24432/C5DG6Q.
@misc{misc_post-operative_patient_82,
author = {Summers,Sharon and Woolery,Linda},
title = {{Post-Operative Patient}},
year = {1993},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5DG6Q}
}Creators
Sharon Summers
Linda Woolery
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.