Risk factors for prediction of delirium at hospital admittance. Issue 4 (1st April 2021)

• Record Type: Journal Article
• Authors: Cano‐Escalera, Guillermo; Graña, Manuel; Irazusta, Jon; Labayen, Idoia; Besga, Ariadna
• Other Names: Chang Victor guestEditor.; Ramachandran Muthu guestEditor.; Li Chung‐Sheng guestEditor.; Zamorano Mariano Rincón guestEditor.; Tomás Rafael Martínez guestEditor.; Vicente José Manuel Ferrández guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-01
• Subjects: boosting classification -- computer aided diagnosis -- delirium -- random forest -- risk factors; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12698
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Aging population in many developed countries, moves the issue of healthy aging at the forefront of the political, scientific and technological concerns. Delirium is a multifactorial disorder that is highly prevalent in hospitalized elderly people that causes complications in the patient care and increases mortality at the hospital and soon after discharge. Early diagnostics would allow improved treatment and prevention for a syndrome that requires very personalized treatment. This paper deals with machine learning based prediction of delirium at hospital admittance as a computer aided diagnostic tool, as well as with the identification of risk factors by means of the variable importance computed by the classifier model building approaches. We achieve almost 0.80 classification accuracy, which is encourages further exploration of improved classifier models. Exploration of variable importance shows that frailty, dementia and some pharmacological factors are relevant risk factors for delirium at hospital admittance.