TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records.

Journal: Computers in biology and medicine
Published Date:

Abstract

BACKGROUND: Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires specific blood tests; however, the triglyceride-glucose (TyG) index can provide a surrogate assessment from routine Electronic Health Record (EHR) data. Since insulin resistance is a multi-factorial condition, to improve its characterisation, this study aims to discover non-trivial clinical factors in EHR data to determine where the insulin-resistance condition is encoded.

Authors

  • Michele Bernardini
  • Micaela Morettini
    Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
  • Luca Romeo
    Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. Electronic address: l.romeo@univpm.it.
  • Emanuele Frontoni
  • Laura Burattini
    Information Engineering Department, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60121, Ancona, Italy.