Healthcare Data Mining: Prediction Inpatient Length of Stay

Author

Peng Liu ; Lei Lei ; Junjie Yin ; Wei Zhang ; Wu Naijun ; El-Darzi, E.

Author_Institution

Sch. of Inf. Manage. & Eng., Shanghai Univ. of Finance & Econ.

fYear

2006

fDate

4-6 Sept. 2006

Firstpage

832

Lastpage

837

Abstract

Data mining approaches have been widely applied in the field of healthcare. At the same time it is recognized that most healthcare datasets are full of missing values. In this paper we apply decision trees, Naive Bayesian classifiers and feature selection methods to a geriatric hospital dataset in order to predict inpatient length of stay, especially for the long stay patients

Keywords

Bayes methods; data mining; decision trees; health care; medical information systems; Naive Bayesian classifiers; data mining; decision trees; feature selection methods; geriatric hospital dataset; healthcare; inpatients; Bayesian methods; Classification tree analysis; Data mining; Decision trees; Hospitals; Intelligent systems; Learning systems; Medical services; Niobium compounds; Robustness; Healthcare data mining; LOS; NBI;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2006 3rd International IEEE Conference on

Conference_Location

London

Print_ISBN

1-4244-0195-X

Type

conf

DOI

10.1109/IS.2006.348528

Filename

4155535

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2399969