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 2 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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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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Flow Analysis of Mastectomy Patients Using Length of Stay: A Single-Center Study

Trunfio,

Improta

2024

BioMedInformatics

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Background: Malignant breast cancer is the most common cancer affecting women worldwide. The COVID-19 pandemic appears to have slowed the diagnostic process, leading to an enhanced use of invasive approaches such as mastectomy. The increased use of a surgical procedure pushes towards an objective analysis of patient flow with measurable quality indicators such as length of stay (LOS) in order to optimize it. Methods: In this work, different regression and classification models were implemented to analyze the total LOS as a function of a set of independent variables (age, gender, pre-op LOS, discharge ward, year of discharge, type of procedure, presence of hypertension, diabetes, cardiovascular disease, respiratory disease, secondary tumors, and surgery with complications) extracted from the discharge records of patients undergoing mastectomy at the ‘San Giovanni di Dio e Ruggi d’Aragona’ University Hospital of Salerno (Italy) in the years 2011–2021. In addition, the impact of COVID-19 was assessed by statistically comparing data from patients discharged in 2018–2019 with those discharged in 2020–2021. Results: The results obtained generally show the good performance of the regression models in characterizing the particular case studies. Among the models, the best at predicting the LOS from the set of variables described above was polynomial regression, with an R2 value above 0.689. The classification algorithms that operated on a LOS divided into 3 arbitrary classes also proved to be good tools, reaching 79% accuracy with the voting classifier. Among the independent variables, both implemented models showed that the ward of discharge, year of discharge, type of procedure and complications during surgery had the greatest impact on LOS. The final focus to assess the impact of COVID-19 showed a statically significant increase in surgical complications. Conclusion: Through this study, it was possible to validate the use of regression and classification models to characterize the total LOS of mastectomy patients. LOS proves to be an excellent indicator of performance, and through its analysis with advanced methods, such as machine learning algorithms, it is possible to understand which of the demographic and organizational variables collected have a significant impact and thus build simple predictors to support healthcare management.

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

Cited by 2 publications

References 47 publications

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

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

Flow Analysis of Mastectomy Patients Using Length of Stay: A Single-Center Study

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