Can machine learning improve randomized clinical trial analysis?

Journal: Seizure
Published Date: Aug 2, 2021

Abstract

PURPOSE: Recently a realistic simulator of patient seizure diaries was developed that can reproduce effects seen in randomized clinical trials (RCTs). RCTs suffer from high costs and statistical inefficiencies. Using realistic simulation and machine learning this study aimed to identify a more statistically efficient outcome metric.

Authors

  • Juan Romero
    Faculty of Computer Science, University of A Coruña, Spain.
  • Sharon Chiang
    From the Department of Neurology and Weill Institute for Neurosciences (S.C., W.C., V.R.R.), University of California, San Francisco; Empatica Inc. (R.W.P.), Boston, MA; Media Lab (R.W.P.), Massachusetts Institute of Technology, Cambridge; Seizure Tracker, LLC (R.M.), Annandale, VA; Department of Neurology (G.A.W.), Mayo Clinic, Rochester, MN; and Department of Neurology (D.M.G.), Beth Israel Deaconess Medical Center, Boston, MA. sharon.chiang@ucsf.edu.
  • Daniel M Goldenholz
    Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.

Keywords

External Resources

Popular Topics
Recent Journals