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Cognitive biomarker prioritization in Alzheimer’s Disease using brain morphometric data
- Source :
- BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-11 (2020), BMC Medical Informatics and Decision Making
- Publication Year :
- 2020
- Publisher :
- BMC, 2020.
-
Abstract
- Background Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method We adapt a newly developed learning-to-rank approach $${\mathtt {PLTR}}$$ PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend $${\mathtt {PLTR}}$$ PLTR to better separate the most effective cognitive assessments and the less effective ones. Results Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.
- Subjects :
- Computer science
Bioinformatics
Population
Health Informatics
Machine learning
computer.software_genre
lcsh:Computer applications to medicine. Medical informatics
Health informatics
Cross-validation
03 medical and health sciences
0302 clinical medicine
Cognition
Alzheimer Disease
Image Interpretation, Computer-Assisted
Humans
Cognitive Dysfunction
Alzheimer’s Disease
education
Set (psychology)
030304 developmental biology
0303 health sciences
education.field_of_study
business.industry
Health Policy
Brain
Computational Biology
Precision medicine
Magnetic Resonance Imaging
Computer Science Applications
Cognitive test
Learning to rank
lcsh:R858-859.7
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Biomarkers
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 14726947
- Volume :
- 20
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Medical Informatics and Decision Making
- Accession number :
- edsair.doi.dedup.....9f0722faf1fe798f7f8121fd691dbec2