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      Predicting and explaining absenteeism risk in hospital patients before and during COVID-19

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          Abstract

          In order to address one of the most challenging problems in hospital management – patients’ absenteeism without prior notice – this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients’ profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.

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          Most cited references43

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          Longitudinal data analysis using generalized linear models

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            Akaike's information criterion in generalized estimating equations.

            W. Pan (2001)
            Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.
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              The COVID-19 pandemic

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                Author and article information

                Journal
                Socioecon Plann Sci
                Socioecon Plann Sci
                Socio-Economic Planning Sciences
                The Author(s). Published by Elsevier Ltd.
                0038-0121
                0038-0121
                28 February 2023
                28 February 2023
                : 101549
                Affiliations
                [1]CIICESI, ESTG, Politecnico do Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
                Author notes
                [* ]Corresponding author.
                Article
                S0038-0121(23)00049-6 101549
                10.1016/j.seps.2023.101549
                9972778
                37255583
                d36273f3-62b6-4101-b1bf-804d8dce4d3c
                © 2023 The Author(s)

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 2 August 2022
                : 15 February 2023
                : 22 February 2023
                Categories
                Article

                62j05,62p25,62p10,68t10,patients absenteeism,risk factors,logistic model,explainable model,cart algorithm,covid-19

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