2021
Neto, C.; Senra, F.; Leite, J.; Rei, N.; Rodrigues, R.; Ferreira, D.; Machado, J.
Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients Journal Article
Em: Journal of Medical Systems, vol. 45, não 1, 2021, ISSN: 01485598, (cited By 11).
Resumo | Links | BibTeX | Etiquetas: acarbose; acetohexamide; chlorpropamide; glibenclamide; glimepiride; glipizide; insulin; metformin; miglitol; nateglinide; pioglitazone; repaglinide; rosiglitazone; tolazamide; tolbutamide; troglitazone, Algorithms; Data Mining; Diabetes Mellitus; Humans; Patient Discharge; Patient Readmission; Retrospective Studies; Risk Factors
@article{Neto2021,
title = {Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients},
author = {C. Neto and F. Senra and J. Leite and N. Rei and R. Rodrigues and D. Ferreira and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098865327&doi=10.1007%2fs10916-020-01686-4&partnerID=40&md5=3c6725a7ffa1a4f6d6a0a99c3990de9d},
doi = {10.1007/s10916-020-01686-4},
issn = {01485598},
year = {2021},
date = {2021-01-01},
journal = {Journal of Medical Systems},
volume = {45},
number = {1},
publisher = {Springer},
abstract = {Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.},
note = {cited By 11},
keywords = {acarbose; acetohexamide; chlorpropamide; glibenclamide; glimepiride; glipizide; insulin; metformin; miglitol; nateglinide; pioglitazone; repaglinide; rosiglitazone; tolazamide; tolbutamide; troglitazone, Algorithms; Data Mining; Diabetes Mellitus; Humans; Patient Discharge; Patient Readmission; Retrospective Studies; Risk Factors},
pubstate = {published},
tppubtype = {article}
}
Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.