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Article

A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome

1
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy
2
Department of Medical Science, University of Ferrara, Via Fossato di Mortara 64B, 44121 Ferrara, Italy
3
Department of Actuarial Sciences, Hacettepe University, Ankara 06800, Turkey
4
Health Professional Management Service (DPS) of the University Hospital of Padova, 35128 Padova, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Francisco José Tarazona Santabalbina, José Viña, Sebastià Josep Santaeugènia Gonzàlez and José Augusto García Navarro
Int. J. Environ. Res. Public Health 2021, 18(13), 7105; https://doi.org/10.3390/ijerph18137105
Received: 26 April 2021 / Revised: 11 June 2021 / Accepted: 14 June 2021 / Published: 2 July 2021
(This article belongs to the Special Issue Health Care for Older Adults)
Delirium is a psycho-organic syndrome common in hospitalized patients, especially the elderly, and is associated with poor clinical outcomes. This study aims to identify the predictors that are mostly associated with the risk of delirium episodes using a machine learning technique (MLT). A random forest (RF) algorithm was used to evaluate the association between the subject’s characteristics and the 4AT (the 4 A’s test) score screening tool for delirium. RF algorithm was implemented using information based on demographic characteristics, comorbidities, drugs and procedures. Of the 78 patients enrolled in the study, 49 (63%) were at risk for delirium, 32 (41%) had at least one episode of delirium during the hospitalization (38% in orthopedics and 31% both in internal medicine and in the geriatric ward). The model explained 75.8% of the variability of the 4AT score with a root mean squared error of 3.29. Higher age, the presence of dementia, physical restraint, diabetes and a lower degree are the variables associated with an increase of the 4AT score. Random forest is a valid method for investigating the patients’ characteristics associated with delirium onset also in small case-series. The use of this model may allow for early detection of delirium onset to plan the proper adjustment in healthcare assistance. View Full-Text
Keywords: aging; nursing; delirium; machine learning technique; random forest aging; nursing; delirium; machine learning technique; random forest
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MDPI and ACS Style

Ocagli, H.; Bottigliengo, D.; Lorenzoni, G.; Azzolina, D.; Acar, A.S.; Sorgato, S.; Stivanello, L.; Degan, M.; Gregori, D. A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome. Int. J. Environ. Res. Public Health 2021, 18, 7105. https://doi.org/10.3390/ijerph18137105

AMA Style

Ocagli H, Bottigliengo D, Lorenzoni G, Azzolina D, Acar AS, Sorgato S, Stivanello L, Degan M, Gregori D. A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome. International Journal of Environmental Research and Public Health. 2021; 18(13):7105. https://doi.org/10.3390/ijerph18137105

Chicago/Turabian Style

Ocagli, Honoria, Daniele Bottigliengo, Giulia Lorenzoni, Danila Azzolina, Aslihan S. Acar, Silvia Sorgato, Lucia Stivanello, Mario Degan, and Dario Gregori. 2021. "A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome" International Journal of Environmental Research and Public Health 18, no. 13: 7105. https://doi.org/10.3390/ijerph18137105

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