A machine learning approach for the prediction of pulmonary hypertension.

Journal: PloS one
Published Date: Oct 25, 2019

Abstract

BACKGROUND: Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters.

Authors

  • Andreas Leha
    Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
  • Kristian Hellenkamp
    Clinic for Cardiology and Pulmonology/Heart Center, University Medical Center Göttingen, Göttingen, Germany.
  • Bernhard Unsöld
    Department of Internal Medicine II, University of Regensburg, Regensburg, Germany.
  • Sitali Mushemi-Blake
    King's College London British Heart Foundation Centre, School of Cardiovascular Medicine & Sciences, London, England, United Kingdom.
  • Ajay M Shah
    King's College London British Heart Foundation Centre, School of Cardiovascular Medicine & Sciences, London, England, United Kingdom.
  • Gerd Hasenfuß
    DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Robert-Koch str. 40, 37075 Göttingen, Germany.
  • Tim Seidler
    DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Robert-Koch str. 40, 37075 Göttingen, Germany.

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