Development and evaluation of ensemble-based classification models for predicting unplanned hospital readmissions after hysterectomy
Eigner I, Reischl D, Bodendorf F (2018)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2018
Conference Proceedings Title: ACIS 2018 Proceedings
Event location: Sydney
URI: https://aisel.aisnet.org/acis2018/62/
DOI: 10.5130/acis2018.cg
Abstract
Unplanned hospital readmissions are a key indicator of quality in healthcare and can lead to high unnecessary costs for the hospital due to additional required resources or reduced payments by insurers or governments. Predictive analytics can support the identification of patients at high-risk for readmission early on to enable timely interventions. In Australia, hysterectomies present the 2nd highest observed readmission rates of all surgical procedures in public hospitals. Prior research so far only focuses on developing explanatory models to identify associated risk factors for past patients. In this study, we develop and compare 24 prediction models using state-of-the-art sampling and ensemble methods to counter common problems in readmission prediction, such as imbalanced data and poor performance of individual classifiers. The application and evaluation of these models are presented, resulting in an excellent predictive power with under- and oversampling and an additional slight increase in performance when combined with ensemble methods.
Authors with CRIS profile
Isabella Eigner
Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich
Daniel Reischl
Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich
Freimut Bodendorf
Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich
How to cite
APA:
Eigner, I., Reischl, D., & Bodendorf, F. (2018). Development and evaluation of ensemble-based classification models for predicting unplanned hospital readmissions after hysterectomy. In ACIS 2018 Proceedings. Sydney, AU.
MLA:
Eigner, Isabella, Daniel Reischl, and Freimut Bodendorf. "Development and evaluation of ensemble-based classification models for predicting unplanned hospital readmissions after hysterectomy." Proceedings of the ACIS 2018, Sydney 2018.
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