| Title: | Hospital Readmission Data for Patients with Diabetes |
| Version: | 0.1.0 |
| Description: | Clinical care data from 130 U.S. hospitals in the years 1999-2008 adapted from the study Strack et al. (2014) <doi:10.1155/2014/781670>. Each row describes an "encounter" with a patient with diabetes, including variables on demographics, medications, patient history, diagnostics, payment, and readmission. |
| License: | MIT + file LICENSE |
| Suggests: | knitr |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Depends: | R (≥ 2.10) |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2023-12-07 14:40:43 UTC; simoncouch |
| Author: | Simon Couch [aut, cre] |
| Maintainer: | Simon Couch <simonpatrickcouch@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-12-07 16:20:05 UTC |
Hospital Readmission Data for Patients with Diabetes
Description
Clinical care data from 130 U.S. hospitals in years 1999-2008. Each row describes an "encounter" with a patient with diabetes, including variables on demographics, medications, patient history, diagnostics, payment, and readmission.
Usage
readmission
Format
A data frame with 71,515 rows and 12 columns:
- readmitted
Whether the patient was readmitted within the 30 days following discharge. A factor with levels
"Yes"and"No".- race
Reported race of the patient. Source data does not document data collection strategy. A factor with levels
"African American","Asian","Caucasian","Hispanic","Other", and"Unknown".- sex
Reported sex of the patient. Source data does not document data collection strategy. A factor with levels
"Female"and"Male".- age
Age range for the patient, binned in 10-year intervals. A factor with levels
"[0-10)","[10-20)","[20-30)","[30-40)","[40-50)","[50-60)","[60-70)","[70-80)","[80-90)", and"[90-100)".- admission_source
Whether the patient was referred from a physician, admitted via the ER, or arrived via some other source. A factor with levels
"Emergency","Other", and"Referral".- blood_glucose
Results from an A1C test, estimating the patient's average blood sugar over the past 2-3 months. Higher estimated average blood glucose levels are linked to diabetes complications. A factor with levels
"Normal","High", and"Very High", and many missing values.- insurer
The health insurance provider (or lack thereof, via
"Self-Pay") for the patient. A factor with levels"Medicaid","Medicare","Private", and"Self-Pay", and many missing values.- duration
Number of days in the hospital between admission and discharge.
- n_previous_visits
Number of emergency, inpatient, and outpatient visits in the year preceding the encounter.
- n_diagnoses
"Number of diagnoses entered to the system" during the encounter.
- n_procedures
"Number of procedures (other than lab tests) performed" during the encounter.
- n_medications
"Number of distinct generic names administered" during the encounter.
Source
Original source data from the following paper (CC BY 3.0):
Strack, B., DeShazo, J. P., Gennings, C., Olmo, J. L., Ventura, S., Cios, K. J., & Clore, J. N. 2014. Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed research international, 781670. doi:10.1155/2014/781670.
Shared freely through the UCI Machine Learning Repository (CC BY 4.0):
Clore, J., Cios, K., DeShazo, J. P., and Strack, B. 2014. Diabetes 130-US hospitals for years 1999-2008. UCI Machine Learning Repository. doi:10.24432/C5230J.
Downloaded from resources shared by the Fairlearn team (MIT):
Weerts, H., DudÃk M., Edgar, R., Jalali, A., Lutz, R., & Madaio, M. 2023. Fairlearn: Assessing and Improving Fairness of AI Systems. Journal of Machine Learning Research, 24(257):1-8.
Examples
str(readmission)
head(readmission)