Application of the PredictAD Decision Support Tool to a Danish Cohort of Patients with Alzheimer's Disease and Other Dementias

Anja Simonsen, Jussi Mattila, Anne-Mette Hejl, Ellen Garde, Mark van Gils, Carsten Thomsen, Jyrki Lötjönen, Hilkka Soininen, Gunhild Waldemar

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

Abstract

The diagnosis of Alzheimer's disease (AD) is based on an ever-increasing body of data and knowledge making it a complex task. The PredictAD tool integrates heterogeneous patient data using an interactive user interface to provide decision support. The aim of this project was to investigate the performance of the tool in distinguishing AD from non-AD dementia using a realistic clinical dataset. Methods: We retrieved clinical data from a group of patients diagnosed with AD (n = 72), vascular dementia (VaD, n = 30), frontotemporal dementia (FTD, n = 25) or dementia with Lewy bodies (DLB, n = 14) at the Copenhagen Memory Clinic at Rigshospitalet. Three classification methods were applied to the data in order to differentiate between AD and a group of non-AD dementias. The methods were the PredictAD tool's Disease State Index (DSI), the naïve Bayesian classifier and the random forest. Results: The DSI performed best for this realistic dataset with an accuracy of 76.6% compared to the accuracies for the naïve Bayesian classifier and random forest of 67.4 and 66.7%, respectively. Furthermore, the DSI differentiated between the four diagnostic groups with a p value of
Original languageEnglish
Pages (from-to)207-213
JournalDementia and Geriatric Cognitive Disorders
Volume37
Issue number3-4
DOIs
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed

Keywords

  • Alzheimer’s disease
  • Dementia
  • Software agents
  • Differential diagnosis
  • Decision support

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