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Vol. 10, No. 1-4, 2012
Issue release date: April 2012
Open Access Gateway
Neurodegenerative Dis 2012;10:149–152
(DOI:10.1159/000332600)

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

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.


 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 hilkka.soininen@uef.fi


 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 (www.loni.ucla.edu\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 http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf.

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: 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.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

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.



 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 hilkka.soininen@uef.fi


 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 (www.loni.ucla.edu\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 http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf.

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: http://www.karger.com/NDD


Open Access License / Drug Dosage

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.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

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.