Effect of Nursing Assessment on Predictive Delirium Models in Hospitalised Patients

Sai Veeranki (Speaker), Dieter Hayn, Diether Kramer, Stefanie Jauk, Günter Schreier

Research output: Chapter in Book or Conference ProceedingsConference Proceedings with Oral Presentation


Delirium is an acute neuropsychiatric syndrome which is common in elderly patients during their hospitalisation and is associated with an increased mortality and morbidity. Since delirium is a) often underdiagnosed and b) preventable if early signs are detected,igh expectations are set in delirium risk assessment during hospital admission. In our latest studies, we showed that delirium prediction using machine learning algorithms is possible based on the patients´ health history. The aim of this study is to compare the influence of nursing assessment data on prediction models with clinical and demographic data. We approached the problem by a) comparing the performance of predictive models including nursing data with models based on clinical and demographic data only and b) analysing the feature importance of all available features. From our results we concluded that nursing assessment data can improve the performance of delirium prediction models better than demographic, laboratory, diagnosis, procedures, and previous transfers´ data alone.
Original languageEnglish
Title of host publicationHealth Informatics Meets eHealth (Serie: Studies in Health Technology and Informatics)
EditorsGünter Schreier, Dieter Hayn
PublisherIOS Press
Number of pages8
Publication statusPublished - 2018
EventeHealth 2018 - "Health informatics meets eHealth" Konferenz -
Duration: 8 May 20189 May 2018


ConferenceeHealth 2018 - "Health informatics meets eHealth" Konferenz

Research Field

  • Exploration of Digital Health


  • Delirium
  • Predictive Analytics
  • Nursing Assessment


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