Software tool for improved prediction of Alzheimer's disease

Hilkka Soininen; Jussi Mattila; Juha Koikkalainen; Mark van Gils; A. Hviid Simonsen; Gunhild Waldemar; Daniel Rueckert; Lennart Thurfjell; Jyrki Lötjönen

doi:10.1159/000332600

Software tool for improved prediction of Alzheimer's disease

Hilkka Soininen (Corresponding Author), Jussi Mattila, Juha Koikkalainen, Mark van Gils, A. Hviid Simonsen, Gunhild Waldemar, Daniel Rueckert, Lennart Thurfjell, Jyrki Lötjönen

Research output: Contribution to journal › Article › Scientific › peer-review

12 Citations (Scopus)

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.

Original language	English
Pages (from-to)	149-152
Journal	Neurodegenerative Diseases
Volume	10
Issue number	1-4
DOIs	https://doi.org/10.1159/000332600
Publication status	Published - 2012
MoE publication type	A1 Journal article-refereed
Event	10th International Conference AD/PD - Barcelona, Spain Duration: 9 Mar 2011 → 13 Mar 2011

Keywords

Alzheimer's disease
biomarker
decision support
memory
mild cognitive impairment

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10.1159/000332600Licence: CC BY-NC

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title = "Software tool for improved prediction of Alzheimer's disease",

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.",

keywords = "Alzheimer's disease, biomarker, decision support, memory, mild cognitive impairment",

author = "Hilkka Soininen and Jussi Mattila and Juha Koikkalainen and {van Gils}, Mark and {Hviid Simonsen}, A. and Gunhild Waldemar and Daniel Rueckert and Lennart Thurfjell and Jyrki L{\"o}tj{\"o}nen",

year = "2012",

doi = "10.1159/000332600",

language = "English",

volume = "10",

pages = "149--152",

journal = "Neurodegenerative Diseases",

issn = "1660-2854",

publisher = "S. Karger",

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note = "10th International Conference AD/PD ; Conference date: 09-03-2011 Through 13-03-2011",

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TY - JOUR

T1 - Software tool for improved prediction of Alzheimer's disease

AU - Soininen, Hilkka

AU - Mattila, Jussi

AU - Koikkalainen, Juha

AU - van Gils, Mark

AU - Hviid Simonsen, A.

AU - Waldemar, Gunhild

AU - Rueckert, Daniel

AU - Thurfjell, Lennart

AU - Lötjönen, Jyrki

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - biomarker

KW - decision support

KW - memory

KW - mild cognitive impairment

U2 - 10.1159/000332600

DO - 10.1159/000332600

M3 - Article

SN - 1660-2854

VL - 10

SP - 149

EP - 152

JO - Neurodegenerative Diseases

JF - Neurodegenerative Diseases

IS - 1-4

T2 - 10th International Conference AD/PD

Y2 - 9 March 2011 through 13 March 2011

ER -

Software tool for improved prediction of Alzheimer's disease

Abstract

Keywords

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