Time-to-event modeling for hospital length of stay prediction for COVID-19 patients

التفاصيل البيبلوغرافية
العنوان:	Time-to-event modeling for hospital length of stay prediction for COVID-19 patients
المؤلفون:	Yuxin Wen, Md Fashiar Rahman, Yan Zhuang, Michael Pokojovy, Honglun Xu, Peter McCaffrey, Alexander Vo, Eric Walser, Scott Moen, Tzu-Liang (Bill) Tseng
المصدر:	Machine Learning with Applications, Vol 9, Iss , Pp 100365- (2022)
بيانات النشر:	Elsevier, 2022.
سنة النشر:	2022
المجموعة:	LCC:Cybernetics LCC:Electronic computers. Computer science
مصطلحات موضوعية:	Length of stay, Survival analysis, Time-to-event modeling, Deep learning, COVID-19, Cybernetics, Q300-390, Electronic computers. Computer science, QA75.5-76.95
الوصف:	Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.
نوع الوثيقة:	article
وصف الملف:	electronic resource
اللغة:	English
تدمد:	2666-8270
Relation:	http://www.sciencedirect.com/science/article/pii/S2666827022000603; https://doaj.org/toc/2666-8270
DOI:	10.1016/j.mlwa.2022.100365
URL الوصول:	https://doaj.org/article/e5f0b52831bf4976a1697e422f92afea
رقم الأكسشن:	edsdoj.5f0b52831bf4976a1697e422f92afea
قاعدة البيانات:	Directory of Open Access Journals

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الوصف
تدمد:	26668270
DOI:	10.1016/j.mlwa.2022.100365