Eigner I, Cooney A (2020)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2020
Publisher: Springer International Publishing
Edited Volumes: Delivering Superior Health and Wellness Management with IoT and Analytics
ISBN: 978-3-030-17347-0
URI: https://link.springer.com/chapter/10.1007/978-3-030-17347-0_12
DOI: 10.1007/978-3-030-17347-0_12
Research on predicting unplanned readmissions in hospitals is becoming more popular with a larger amount of hospital data becoming available. To gain an in-depth observation of recent insights in the field, a literature review analysing contributions between the years of 2005 and 2017 is conducted. The aggregated results show the most important risk factors included in prediction models so far, evaluation metrics of both all-cause and diagnosis-specific prediction models as well as the most prominent classification methods used in this context. Furthermore, the development of research on predicting unplanned patient readmissions over time is shown, and current gaps are identified.
APA:
Eigner, I., & Cooney, A. (2020). A Literature Review on Predicting Unplanned Patient Readmissions. In Wickramasinghe N., Bodendorf F. (Eds.), Delivering Superior Health and Wellness Management with IoT and Analytics. Springer International Publishing.
MLA:
Eigner, Isabella, and Andrew Cooney. "A Literature Review on Predicting Unplanned Patient Readmissions." Delivering Superior Health and Wellness Management with IoT and Analytics. Ed. Wickramasinghe N., Bodendorf F., Springer International Publishing, 2020.
BibTeX: Download