Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study.

Journal: Frontiers in digital health
Published Date: Jan 8, 2024

Abstract

BACKGROUND: Recently, crowding in emergency departments (EDs) has become a recognised critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds available in inpatients units and EDs. The length of stay in the ED (ED-LOS) has been found to be a significant indicator of ED bottlenecks. The time a patient spends in the ED is quantified by measuring the ED-LOS, which can be influenced by inefficient care processes and results in increased mortality and health expenditure. Therefore, it is critical to understand the major factors influencing the ED-LOS through forecasting tools enabling early improvements.

Authors

  • Carlo Ricciardi
    Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.
  • Marta Rosaria Marino
    Department of Public Health, University of Naples "Federico II", Naples, Italy.
  • Teresa Angela Trunfio
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
  • Massimo Majolo
    Department of Public Health, University of Naples "Federico II", Naples, Italy.
  • Maria Romano
    Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.
  • Francesco Amato
    Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.
  • Giovanni Improta
    Department of Public Health, University of Naples "Federico II", Naples, Italy.

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