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Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease.
Harvey, Joshua; Reijnders, Rick A; Cavill, Rachel; Duits, Annelien; Köhler, Sebastian; Eijssen, Lars; Rutten, Bart P F; Shireby, Gemma; Torkamani, Ali; Creese, Byron; Leentjens, Albert F G; Lunnon, Katie; Pishva, Ehsan.
Afiliación
  • Harvey J; Medical School, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
  • Reijnders RA; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
  • Cavill R; Department of Advanced Computing Sciences, FSE, Maastricht University, Maastricht, The Netherlands.
  • Duits A; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
  • Köhler S; Department of Medical Psychology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Eijssen L; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
  • Rutten BPF; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
  • Shireby G; Department of Bioinformatics-BiGCaT, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, The Netherlands.
  • Torkamani A; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
  • Creese B; Medical School, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
  • Leentjens AFG; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA.
  • Lunnon K; Medical School, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
  • Pishva E; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
NPJ Parkinsons Dis ; 8(1): 150, 2022 Nov 07.
Article en En | MEDLINE | ID: mdl-36344548
Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: NPJ Parkinsons Dis Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: NPJ Parkinsons Dis Año: 2022 Tipo del documento: Article