% 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”.

@ARTICLE{Mller:888454,
      author       = {Müller, Tamara T. and Lio, Pietro},
      title        = {{PECLIDES} {N}euro: {A} {P}ersonalisable {C}linical
                      {D}ecision {S}upport {S}ystem for {N}eurological {D}iseases},
      journal      = {Frontiers in artificial intelligence},
      volume       = {3},
      issn         = {2624-8212},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2020-04922},
      pages        = {23},
      year         = {2020},
      abstract     = {Neurodegenerative diseases such as Alzheimer's and
                      Parkinson's impact millions of people worldwide. Early
                      diagnosis has proven to greatly increase the chances of
                      slowing down the diseases' progression. Correct diagnosis
                      often relies on the analysis of large amounts of patient
                      data, and thus lends itself well to support from machine
                      learning algorithms, which are able to learn from past
                      diagnosis and see clearly through the complex interactions
                      of a patient's symptoms and data. Unfortunately, many
                      contemporary machine learning techniques fail to reveal
                      details about how they reach their conclusions, a property
                      considered fundamental when providing a diagnosis. Here we
                      introduce our Personalisable Clinical Decision Support
                      System (PECLIDES), an algorithmic process formulated to
                      address this specific fault in diagnosis detection. PECLIDES
                      provides a clear insight into the decision-making process
                      leading to a diagnosis, making it a gray box model. Our
                      algorithm enriches the fundamental work of Masheyekhi and
                      Gras in data integration, personal medicine, usability,
                      visualization, and interactivity.Our decision support system
                      is an operation of translational medicine. It is based on
                      random forests, is personalisable and allows a clear insight
                      into the decision-making process. A well-structured rule set
                      is created and every rule of the decision-making process can
                      be observed by the user (physician). Furthermore, the user
                      has an impact on the creation of the final rule set and the
                      algorithm allows the comparison of different diseases as
                      well as regional differences in the same disease. The
                      algorithm is applicable to various decision problems. In
                      this paper we will evaluate it on diagnosing neurological
                      diseases and therefore refer to the algorithm as PECLIDES
                      Neuro1.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33733142},
      UT           = {WOS:000751673300023},
      doi          = {10.3389/frai.2020.00023},
      url          = {https://juser.fz-juelich.de/record/888454},
}