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Neurodegenerative Dis 2012;10:149–152

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
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
email Corresponding Author

 goto top of outline Key Words

  • Alzheimer’s disease
  • Biomarker
  • Decision support
  • Mild cognitive impairment
  • Memory

 goto top of outline Abstract

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.

Copyright © 2011 S. Karger AG, Basel

 goto top of outline References
  1. Cowan N: The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav Brain Sci 2001;24:87–114.
  2. Bergus GR, Chapman GB, Gjerde C, Elstein AS: Clinical reasoning about new symptoms in the face of pre-existing disease: sources of error and order effects. Fam Med 1995;27:314–320.
  3. Speier C, Valacich JS, Vessey I: The influence of task interruption on individual decision making: an information overload perspective. Decision Sci 1999;30:337–360.

    External Resources

  4. Musen MA, Sharar Y, Shortliffe EH: Clinical Decision-Support Systems. Biomedical Informatics. New York, Springer, 2006, pp 698–736.
  5. Mattila J, Koikkalainen J, Virkki A, Simonsen A, van Gils M, Waldemar G, Soininen H, Lötjönen J: A disease state fingerprint for evaluation of Alzheimer’s diseases. J Alzheimers Dis, in press.

 goto top of outline Author Contacts

Prof. Hilkka Soininen
Department of Neurology
University of Eastern Finland
PO Box 1627, FI–70211 Kuopio (Finland)
Tel. +358 17 173 012, E-Mail

 goto top of outline Article Information

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (\ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. ADNI investigators: a complete listing is available at

Received: June 30, 2011
Accepted after revision: September 1, 2011
Published online: December 9, 2011
Number of Print Pages : 4
Number of Figures : 1, Number of Tables : 1, Number of References : 5

 goto top of outline Publication Details

Neurodegenerative Diseases

Vol. 10, No. 1-4, Year 2012 (Cover Date: April 2012)

Journal Editor: Nitsch R.M. (Zürich), Hock C. (Zürich)
ISSN: 1660-2854 (Print), eISSN: 1660-2862 (Online)

For additional information:

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