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

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Biomedical and Computational Biology (BECB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13637))

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Abstract

The increase in emergency access causes overcrowding, which leads to an increase in waiting times and consequently in the rate of abandonment. LWBS patients are the patients who register the cure, but then leave ED without any visit to a doctor. Studying the number of LWBS is effective to improve first aid flows and better manage healthcare staff. In this work we investigate some of the factors that can contribute to the increase of LWBS patients in the ED. Data were collected at the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and at the hospital “A.O.R.N. A. Cardarelli” of Naples (Italy) and then analyzed through advanced machine learning algorithms. The results demonstrate once again that ML is a valuable predictor.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Technology, University of Study of Naples “Federico II”, Naples, Italy

    Alfonso Maria Ponsiglione

  2. Department of Public Health, University of Naples “Federico II”, Naples, Italy

    Marta Rosaria Marino & Giovanni Improta

  3. A.O.R.N. “Antonio Cardarelli”, Naples, Italy

    Eliana Raiola & Giuseppe Russo

  4. San Giovanni di Dio e Ruggi d’Aragona” University Hospital, Salerno, Italy

    Anna Borrelli

  5. Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, Naples, Italy

    Giovanni Improta

Authors
  1. Alfonso Maria Ponsiglione
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  2. Marta Rosaria Marino
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  3. Eliana Raiola
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  4. Giuseppe Russo
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  5. Anna Borrelli
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  6. Giovanni Improta
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Corresponding author

Correspondence to Marta Rosaria Marino .

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Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shiping Wen

  2. Nanchang Hangkong University, Nanchang, China

    Cihui Yang

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Ponsiglione, A.M., Marino, M.R., Raiola, E., Russo, G., Borrelli, A., Improta, G. (2023). Predictive Models for Studying Emergency Department Abandonment Rates: A Bicentric Study. In: Wen, S., Yang, C. (eds) Biomedical and Computational Biology. BECB 2022. Lecture Notes in Computer Science(), vol 13637. Springer, Cham. https://doi.org/10.1007/978-3-031-25191-7_41

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_41

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