Paper accepted at Journal of Medical Internet Research

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Our research article titled “Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals with Prediabetes or Diabetes: Retrospective Cohort Study” has been accepted in the Journal of Medical Internet Research. In this work, we study how machine learning methods can be used to predict complications for patients with diabetes. We made use of electronic health records to identify individuals with diabetes or prediabetes and, subsequently, analyzed which of these individuals developed complications. In this work, we compared two ML models, namely logistic regression and gradient boosted decision trees (GBDTs).

This is joint work with Simon Schallmoser and Stefan Feuerriegel from LMU Munich, Thomas Züger and Christoph Stettler from University of Bern, and Maytal Saar-Tsechansky from University of Texas at Austin.