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المؤلفGao, Ruobin
المؤلفCheng, Wen Xin
المؤلفSuganthan, P. N.
المؤلفYuen, Kum Fai
تاريخ الإتاحة2023-02-12T10:39:12Z
تاريخ النشر2022-10-01
اسم المنشورIEEE Journal of Biomedical and Health Informatics
المعرّفhttp://dx.doi.org/10.1109/JBHI.2022.3172956
الاقتباسGao, 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.‏
الرقم المعياري الدولي للكتاب21682194
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85132532688&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/39993
الملخصHospitals 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعDeep learning
forecasting
forecasting
machine learning
randomized neural networks
العنوانInpatient Discharges Forecasting for Singapore Hospitals by Machine Learning
النوعArticle
الصفحات4966-4975
رقم العدد10
رقم المجلد26
dc.accessType Abstract Only


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