Machine Learning Approach to Predict Dementia Progression

 
   Lead Investigator:    Chih-Lin Chi
   Institution      :    University of Minnesota, School of Nursing
   E-Mail           :    cchi@umn.edu
   Proposal ID      :    591


   Publications:


1  Chi CL, Zeng W, Oh W, Borson S
   A Translational Research Strategy to Individualize Prediction of Long-Term Cognitive Trajectory
   Abstract  Alzheimers Dement,12:P847,2016,

   PUBLISHED





   Proposal Description:
   Accurate, individualized prediction of cognitive decline progression and speed    of progression of established dementia is increasingly important for research     on disease-modifying treatments and therapeutic interventions, and for clinical   management of patients. In this proposed research, we aims to develop a           machine-learning approach to predict cognitive decline progression using NACC     data. Leveraging on our preliminary experience using diverse participant          information including genetic, imaging, biomarker, and neuropsychological data    simultaneously in various time points, we plan to predict transition to           dementia to support decisions in three clinical scenarios: short-term             prediction (half or one year) based on a single assessment  (simulating a         ???new patient??? visit) short-term prediction based on information from two      time points (simulating a ???follow up??? visit), and long-term (multiple         years) prediction (simulating ongoing follow-up with repeated opportunities for   assessment).