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AuthorGao, Ruobin
AuthorCheng, Wen Xin
AuthorSuganthan, P. N.
AuthorYuen, Kum Fai
Available date2023-02-12T10:39:12Z
Publication Date2022-10-01
Publication NameIEEE Journal of Biomedical and Health Informatics
Identifierhttp://dx.doi.org/10.1109/JBHI.2022.3172956
CitationGao, R., Cheng, W. X., Suganthan, P. N., & Yuen, K. F. (2022). Inpatient discharges forecasting for singapore hospitals by machine learning. IEEE Journal of Biomedical and Health Informatics, 26(10), 4966-4975.‏
ISSN21682194
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85132532688&origin=inward
URIhttp://hdl.handle.net/10576/39993
AbstractHospitals can predetermine the admission rate and facilitate resource allocation based on valid emergency requests and bed capacity estimation. The excess unoccupied beds can be determined with the help of forecasting the number of discharged patients. Extracting predictive features and mining the temporal patterns from historical observations are crucial for accurate and reliable forecasts. Machine learning algorithms have demonstrated the ability to learn temporal knowledge and make predictions for unseen inputs. This paper utilizes several machine learning algorithms to forecast the inpatient discharges of Singapore hospitals and compare them with statistical methods. A novel ensemble deep learning algorithm based on random vector functional links is established to predict inpatient discharges. The ensemble deep learning framework is optimized in a greedy layer-wise fashion. Several forecasting metrics and statistical tests are utilized to demonstrate the proposed method's superiority. The proposed algorithm statistically outperforms the benchmark with a ranking of 1.875. Finally, practical implications and future directions are discussed.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDeep learning
forecasting
forecasting
machine learning
randomized neural networks
TitleInpatient Discharges Forecasting for Singapore Hospitals by Machine Learning
TypeArticle
Pagination4966-4975
Issue Number10
Volume Number26
dc.accessType Abstract Only


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