Predictive Models for Studying Emergency Department Abandonment Rates: A Bicentric Study

Ponsiglione, Alfonso Maria; Marino, Marta Rosaria; Raiola, Eliana; Russo, Giuseppe; Borrelli, Anna; Improta, Giovanni

doi:10.1007/978-3-031-25191-7_41

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…While ML and simulation, taken separately, have been widely investigated in the literature (e.g. to forecast complications and adverse events to achieve timely and more accurate clinical decisions [14][15][16][17][18] or with the aim of improving the management of healthcare emergency processes [19][20][21] and reduce the length of hospital stay (LOS) [20,[22][23][24][25][26][27][28]), there is only a limited subset of studies [14,29] that exploit a hybrid ML-powered simulation methodology in the healthcare field.…”

Section: Introductionmentioning

confidence: 99%

Combining simulation models and machine learning in healthcare management: strategies and applications

Ponsiglione,

Zaffino,

Ricciardi

et al. 2024

Prog. Biomed. Eng.

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Simulation models and artificial intelligence are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and artificial intelligence could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and artificial intelligence approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and artificial intelligence as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed artificial intelligence strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.

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