Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients.
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 conve...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 1; pp. 1 - 10 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147997207&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147997207 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2021 vid: 45 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 147997207 147997207 147997207 10.1007/s10916-020-01686-4 147997207 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Different Scenarios for the Prediction of Hospital Readmission of Diabetic Patients. aug: au: Neto, Cristiana Senra, Fábio Leite, Jaime Rei, Nuno Rodrigues, Rui Ferreira, Diana Machado, José affil: Algoritmi Research Center, Braga, Portugal sug: subj: Diabetes Mellitus Prognosis Readmission Data Mining Diabetic Patients Human Quality of Health Care Machine Learning Algorithms Quality Improvement Hospitalization Descriptive Statistics ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|