Forecasting length of stay: Will it be clear or cloudy today?
نویسندگان
چکیده
Patient length of stay (LOS) is a vital metric for hospital operational efficiency, and shorter LOS tied to better patient outcomes improved financial performance. Models that provide accurate, real-time forecasts can help hospitals effectively manage their resources bed capacity. Forecasting perfect problem modern machine learning methods. In this paper, we conduct descriptive literature review studies use methods predict LOS. We searched Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Cochrane databases articles published between 2008 2021 models forecast From 87 identified through keyword search the two using snowball method, used pre-specified inclusion criteria select final 12 in review. The are international, retrospective, carried out during ML development lifecycle. Most approached forecasting as classification problem, with minority opting train regression instead. most frequently included support vector machines, random forests, gradient boosted trees, logistic regressions, neural networks. general, tree-based like forests trees had best performance – stacked combined predictions multiple also performed well. Several natural language processing (NLP) other techniques extract features from unstructured electronic health record data improve model addition feature selection, preprocessing decisions, such careful handling missing resampling address class imbalance, significantly Machine capable impressive accuracy. However, were designed pre-deployment experimental models. As AI applications advance, systematic approach crafting high-quality management monitoring clinical production essential developing precise prediction service. While production, factors drift recognition, monitoring, correction required accurate Future longitudinal must validate these recognize real-world healthcare impact.
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ژورنال
عنوان ژورنال: Intelligence-based medicine
سال: 2022
ISSN: ['2666-5212']
DOI: https://doi.org/10.1016/j.ibmed.2022.100078