For Manuscript Submission, Check or Review Login please go to Submission Websites List.
For the academic login, please select your country in the dropdown list. You will be redirected to verify your credentials.
Software Tool for Improved Prediction of Alzheimer’s DiseaseSoininen 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 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 email@example.com
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