2018
Silva, C.; Oliveira, D.; Peixoto, H.; Machado, J.; Abelha, A.
Data mining for prediction of length of stay of cardiovascular accident inpatients Proceedings Article
Em: A.V., Alexandrov D. A. Chugunov A. V. Boukhanovsky (Ed.): pp. 516-527, Springer Verlag, 2018, ISSN: 18650929, (cited By 5; Conference of 3rd International Conference on Digital Transformation and Global Society, DTGS 2018 ; Conference Date: 30 May 2018 Through 2 June 2018; Conference Code:220939).
Resumo | Links | BibTeX | Etiquetas: Accidents; Decision support systems; Forecasting; Health care; Hospitals; Learning systems, Clinical decision support; Clinical management; Disease progression; Evaluation and analysis; Healthcare sectors; Large amounts of data; Management efficiency; Weka, Data mining
@inproceedings{Silva2018516,
title = {Data mining for prediction of length of stay of cardiovascular accident inpatients},
author = {C. Silva and D. Oliveira and H. Peixoto and J. Machado and A. Abelha},
editor = {Alexandrov D. A. Chugunov A.V. Boukhanovsky A.V.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057139340&doi=10.1007%2f978-3-030-02843-5_43&partnerID=40&md5=f8bd9dee0c6500c60b42f061fb2af9c3},
doi = {10.1007/978-3-030-02843-5_43},
issn = {18650929},
year = {2018},
date = {2018-01-01},
journal = {Communications in Computer and Information Science},
volume = {858},
pages = {516-527},
publisher = {Springer Verlag},
abstract = {The healthcare sector generates large amounts of data on a daily basis. This data holds valuable knowledge that, beyond supporting a wide range of medical and healthcare functions such as clinical decision support, can be used for improving profits and cutting down on wasted overhead. The evaluation and analysis of stored clinical data may lead to the discovery of trends and patterns that can significantly enhance overall understanding of disease progression and clinical management. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a data mining project approach to predict the hospitalization period of cardiovascular accident patients. This provides an effective tool for the hospital cost containment and management efficiency. The data used for this project contains information about patients hospitalized in Cardiovascular Accident’s unit in 2016 for having suffered a stroke. The Weka software was used as the machine learning toolkit. © Springer Nature Switzerland AG 2018.},
note = {cited By 5; Conference of 3rd International Conference on Digital Transformation and Global Society, DTGS 2018 ; Conference Date: 30 May 2018 Through 2 June 2018; Conference Code:220939},
keywords = {Accidents; Decision support systems; Forecasting; Health care; Hospitals; Learning systems, Clinical decision support; Clinical management; Disease progression; Evaluation and analysis; Healthcare sectors; Large amounts of data; Management efficiency; Weka, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
The healthcare sector generates large amounts of data on a daily basis. This data holds valuable knowledge that, beyond supporting a wide range of medical and healthcare functions such as clinical decision support, can be used for improving profits and cutting down on wasted overhead. The evaluation and analysis of stored clinical data may lead to the discovery of trends and patterns that can significantly enhance overall understanding of disease progression and clinical management. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a data mining project approach to predict the hospitalization period of cardiovascular accident patients. This provides an effective tool for the hospital cost containment and management efficiency. The data used for this project contains information about patients hospitalized in Cardiovascular Accident’s unit in 2016 for having suffered a stroke. The Weka software was used as the machine learning toolkit. © Springer Nature Switzerland AG 2018.