% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Kroll:884725,
      author       = {Kroll, Jean-Philippe and Eickhoff, Simon B. and
                      Hoffstaedter, Felix and Patil, Kaustubh R.},
      title        = {{E}volving complex yet interpretable representations:
                      application to {A}lzheimer’s diagnosis and prognosis},
      publisher    = {IEEE},
      reportid     = {FZJ-2020-03219},
      pages        = {-},
      year         = {2020},
      note         = {This 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.},
      abstract     = {With 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},
      month         = {Jul},
      date          = {2020-07-19},
      organization  = {2020 IEEE Congress on Evolutionary
                       Computation (CEC), Glasgow (United
                       Kingdom), 19 Jul 2020 - 24 Jul 2020},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572) / 574 -
                      Theory, modelling and simulation (POF3-574) / HBP SGA1 -
                      Human Brain Project Specific Grant Agreement 1 (720270) /
                      HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907)},
      pid          = {G:(DE-HGF)POF3-572 / G:(DE-HGF)POF3-574 /
                      G:(EU-Grant)720270 / G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1109/CEC48606.2020.9185843},
      url          = {https://juser.fz-juelich.de/record/884725},
}