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000884725 005__ 20210130005919.0
000884725 0247_ $$2doi$$a10.1109/CEC48606.2020.9185843
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000884725 1001_ $$0P:(DE-Juel1)176972$$aKroll, Jean-Philippe$$b0$$eCorresponding author$$ufzj
000884725 1112_ $$a2020 IEEE Congress on Evolutionary Computation (CEC)$$cGlasgow$$d2020-07-19 - 2020-07-24$$wUnited Kingdom
000884725 245__ $$aEvolving complex yet interpretable representations: application to Alzheimer’s diagnosis and prognosis
000884725 260__ $$bIEEE$$c2020
000884725 300__ $$a-
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000884725 500__ $$aThis study was supported by the European Union‘sHorizon 2020 Research and Innovation Programme underGrant Agreement No. 785907 (HBP SGA2) and GrantAgreement No. 7202070 (HBP SGA1). Data collection andsharing for this project was funded by the Alzheimer's DiseaseNeuroimaging Initiative (ADNI) (National Institutes of HealthGrant U01 AG024904) and DOD ADNI (Department ofDefense award number W81XWH-12-2-0012). ADNI isfunded by the National Institute on Aging, the NationalInstitute of Biomedical Imaging and Bioengineering, andthrough generous contributions from the following: AbbVie,Alzheimer’s Association; Alzheimer’s Drug DiscoveryFoundation; Araclon Biotech; BioClinica, Inc.; Biogen;Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate;Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company;EuroImmun; F. Hoffmann-La Roche Ltd and its affiliatedcompany Genentech, Inc.; Fujirebio; GE Healthcare; IXICOLtd.; Janssen Alzheimer Immunotherapy Research &Development, LLC.; Johnson & Johnson PharmaceuticalResearch & Development LLC.; Lumosity; Lundbeck; Merck& Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRxResearch; Neurotrack Technologies; NovartisPharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging;Servier; Takeda Pharmaceutical Company; and TransitionTherapeutics. The Canadian Institutes of Health Research isproviding funds to support ADNI clinical sites in Canada.Private sector contributions are facilitated by the Foundationfor the National Institutes of Health (www.fnih.org). Thegrantee organization is the Northern California Institute forResearch and Education, and the study is coordinated by theAlzheimer’s Therapeutic Research Institute at the Universityof Southern California. ADNI data are disseminated by theLaboratory for Neuro Imaging at the University of SouthernCalifornia.
000884725 520__ $$aWith increasing accuracy and availability of moredata, the potential of using machine learning (ML) methods inmedical and clinical applications has gained considerableinterest. However, the main hurdle in translational use of MLmethods is the lack of explainability, especially when non-linearmethods are used. Explainable (i.e. human-interpretable)methods can provide insights into disease mechanisms but canequally importantly promote clinician-patient trust, in turnhelping wider social acceptance of ML methods. Here, weempirically test a method to engineer complex, yet interpretable,representations of base features via evolution of context-freegrammar (CFG). We show that together with a simple MLalgorithm evolved features provide higher accuracy on severalbenchmark datasets and then apply it to a real word problem ofdiagnosing Alzheimer’s disease (AD) based on magneticresonance imaging (MRI) data. We further demonstrate highperformance on a hold-out dataset for the prognosis of AD.Keywords — grammar evolution, feature representation,interpretability, Alzheimer’s disease, machine learning
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000884725 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x2
000884725 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000884725 588__ $$aDataset connected to CrossRef Conference
000884725 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b1$$ufzj
000884725 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b2$$ufzj
000884725 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b3$$ufzj
000884725 773__ $$a10.1109/CEC48606.2020.9185843
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