Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_FC4021224480
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts.
Journal
Frontiers in neurology
Author(s)
Redolfi A., De Francesco S., Palesi F., Galluzzi S., Muscio C., Castellazzi G., Tiraboschi P., Savini G., Nigri A., Bottini G., Bruzzone M.G., Ramusino M.C., Ferraro S., Gandini Wheeler-Kingshott CAM, Tagliavini F., Frisoni G.B., Ryvlin P., Demonet J.F., Kherif F., Cappa S.F., D'Angelo E.
ISSN
1664-2295 (Print)
ISSN-L
1664-2295
Publication state
Published
Issued date
2020
Peer-reviewed
Oui
Volume
11
Pages
1021
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.
Keywords
Alzheimer's Dementia (AD), Medical Informatics Platform (MIP), biomarkers, diagnostic confidence, disease signature
Pubmed
Web of science
Open Access
Yes
Create date
20/10/2020 9:00
Last modification date
12/01/2022 8:15
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