Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery.

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Published Date: May 1, 2019

Abstract

OBJECTIVES: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic medication cessation (validated proxy for A1c control) in patients with treated T2D who underwent MxS.

Authors

  • Stephen S Johnston
    Epidemiology, Medical Devices, Johnson & Johnson, New Brunswick, NJ, USA. Electronic address: sjohn147@its.jnj.com.
  • John M Morton
    Department of Surgery, Stanford University, Stanford, CA, USA.
  • Iftekhar Kalsekar
    Epidemiology, Medical Devices, Johnson & Johnson, New Brunswick, NJ, USA.
  • Eric M Ammann
    Epidemiology, Medical Devices, Johnson & Johnson, New Brunswick, NJ, USA.
  • Chia-Wen Hsiao
    Ethicon, Johnson & Johnson, Somerville, NJ, USA.
  • Jenna Reps
    Advanced Data Analysis Centre, University of Nottingham, Nottingham, United Kingdom.

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