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Software Tool for Improved Prediction of Alzheimer’s Disease

Soininen H.a · Mattila J.b · Koikkalainen J.b · van Gils M.b · Hviid Simonsen A.c · Waldemar G.c · Rueckert D.d · Thurfjell L.e · Lötjönen J.b · for the Alzheimer’s Disease Neuroimaging Initiative

Author affiliations

aUniversity of Eastern Finland, Kuopio University Hospital, Kuopio, and bVTT Technical Research Centre of Finland, Tampere, Finland; cDepartment of Neurology, Rigshospitalet, Copenhagen, Denmark; dImperial College London, London, UK; eGE Healthcare, Uppsala, Sweden

Corresponding Author

Prof. Hilkka Soininen

Department of Neurology

University of Eastern Finland

PO Box 1627, FI–70211 Kuopio (Finland)

Tel. +358 17 173 012, E-Mail hilkka.soininen@uef.fi

Related Articles for ""

Neurodegenerative Dis 2012;10:149–152

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Background: Diagnostic criteria of Alzheimer’s disease (AD) emphasize the integration of clinical data and biomarkers. In practice, collection and analysis of patient data vary greatly across different countries and clinics. Objective: The goal was to develop a versatile and objective clinical decision support system that could reduce diagnostic errors and highlight early predictors of AD. Methods: Novel data analysis methods were developed to derive composite disease indicators from heterogeneous patient data. Visualizations that communicate these findings were designed to help the interpretation. The methods were implemented with a software tool that is aimed for daily clinical practice. Results: With the tool, clinicians can analyze available patients as a whole, study them statistically against previously diagnosed cases, and characterize the patients with respect to having AD. The tool is able to work with virtually any patient measurement data, as long as they are stored in electronic format or manually entered into the system. For a subset of patients from the test cohort, the tool was able to predict conversion to AD at an accuracy of 93.6%. Conclusion: The software tool developed in this study provides objective information for early detection and prediction of AD based on interpretable visualizations of patient data.

© 2011 S. Karger AG, Basel

Article / Publication Details

First-Page Preview
Abstract of Paper

Received: June 30, 2011
Accepted: September 01, 2011
Published online: December 09, 2011
Issue release date: April 2012

Number of Print Pages: 4
Number of Figures: 1
Number of Tables: 1

ISSN: 1660-2854 (Print)
eISSN: 1660-2862 (Online)

For additional information: http://www.karger.com/NDD

Open Access License / Drug Dosage / Disclaimer

Open Access License: This is an Open Access article licensed under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported license (CC BY-NC) (www.karger.com/OA-license), applicable to the online version of the article only. Distribution permitted for non-commercial purposes only.
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