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Deep Phenotyping of Parkinson's Disease.
Dorsey, E Ray; Omberg, Larsson; Waddell, Emma; Adams, Jamie L; Adams, Roy; Ali, Mohammad Rafayet; Amodeo, Katherine; Arky, Abigail; Augustine, Erika F; Dinesh, Karthik; Hoque, Mohammed Ehsan; Glidden, Alistair M; Jensen-Roberts, Stella; Kabelac, Zachary; Katabi, Dina; Kieburtz, Karl; Kinel, Daniel R; Little, Max A; Lizarraga, Karlo J; Myers, Taylor; Riggare, Sara; Rosero, Spencer Z; Saria, Suchi; Schifitto, Giovanni; Schneider, Ruth B; Sharma, Gaurav; Shoulson, Ira; Stevenson, E Anna; Tarolli, Christopher G; Luo, Jiebo; McDermott, Michael P.
Afiliación
  • Dorsey ER; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Omberg L; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
  • Waddell E; Sage Bionetworks, Seattle, WA, USA.
  • Adams JL; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Adams R; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Ali MR; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
  • Amodeo K; Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA.
  • Arky A; Department of Computer Science, University of Rochester, Rochester, NY, USA.
  • Augustine EF; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
  • Dinesh K; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Hoque ME; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Glidden AM; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
  • Jensen-Roberts S; Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA.
  • Kabelac Z; Department of Computer Science, University of Rochester, Rochester, NY, USA.
  • Katabi D; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Kieburtz K; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Kinel DR; Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Little MA; Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Lizarraga KJ; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Myers T; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
  • Riggare S; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Rosero SZ; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
  • Saria S; School of Computer Science, University of Birmingham, UK.
  • Schifitto G; Massachusetts Institute of Technology, MA, USA.
  • Schneider RB; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Sharma G; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
  • Shoulson I; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
  • Stevenson EA; Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
  • Tarolli CG; Department of Medicine, University of Rochester, Rochester, NY, USA.
  • Luo J; Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA.
  • McDermott MP; Department of Computer Science, Statistics, and Health Policy, Johns Hopkins University, MD, USA.
J Parkinsons Dis ; 10(3): 855-873, 2020.
Article en En | MEDLINE | ID: mdl-32444562
Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Fenotipo / Marcha Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Parkinsons Dis Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Fenotipo / Marcha Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Parkinsons Dis Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos