TY - JOUR
T1 - Machine Learning for Hospital Length-of-Stay Prediction: A Systematic Review of Electronic Health Record Features, Model Architectures, Validation Methods, and Operational Implementation Outcomes
AU - Hiroshi Nakamura
AU - Yuta Kato
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2023
VL - 3
IS - 1
DO - 10.68159/c976828098
SP - 75
N2 - Hospital length-of-stay is a central operational metric for inpatient capacity planning, discharge coordination, and resource allocation. Accurate prediction remains difficult because patient trajectories are heterogeneous, nonlinear, and shaped by evolving clinical events during admission. Traditional statistical models often have limited flexibility for high-dimensional and sequential electronic health record data. Across the literature, there is no settled consensus regarding optimal model architecture, feature representation, validation design, or clinical implementation strategy. This systematic review synthesizes machine learning approaches for hospital length-of-stay prediction published from 2017 to 2022. It focuses on EHR feature types, model architectures, validation methods, interpretability strategies, and reported operational outcomes. A structured review of peer-reviewed literature was conducted using targeted search strings related to machine learning, deep learning, electronic health records, discharge prediction, and hospital length-of-stay. The review included studies across emergency, inpatient, surgical, pediatric, cardiovascular, and intensive care settings. The literature suggests that gradient boosting, random forest, ensemble learning, and recurrent neural networks are common approaches for LOS prediction. However, external validation remains uncommon, prediction horizons vary widely, and operational implementation outcomes are reported less consistently than model development results. Future research should prioritize external validation, prospective implementation studies, standardized outcome definitions, and transparent reporting of workflow barriers. Shared benchmarking datasets and multi-center validation consortia would strengthen comparability across LOS prediction studies.
UR - https://cirpublications.com/c976828098
ER -