Use of Steroid Profiling Combined With Machine Learning for Identification and Subtype Classification in Primary Aldosteronism

Author:

Eisenhofer Graeme12,Durán Claudio3,Cannistraci Carlo Vittorio34,Peitzsch Mirko2,Williams Tracy Ann56,Riester Anna6,Burrello Jacopo5,Buffolo Fabrizio5,Prejbisz Aleksander7,Beuschlein Felix68,Januszewicz Andrzej7,Mulatero Paolo5,Lenders Jacques W. M.19,Reincke Martin6

Affiliation:

1. Department of Internal Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

2. Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

3. Biomedical Cybernetics Group, Biotechnology Center, Center for Molecular and Cellular Bioengineering, Center for Systems Biology Dresden, Department of Physics, Technische Universität Dresden, Dresden, Germany

4. Center for Complex Network Intelligence Laboratory at the Tsinghua Laboratory of Brain and Intelligence, Department of Bioengineering, Tsinghua University, Beijing, China

5. Division of Internal Medicine and Hypertension, Department of Medical Sciences, University of Turin, Turin, Italy

6. Medizinische Klinik und Poliklinik IV, Klinikum der Ludwig-Maximilians-Universität München, Munich, Germany

7. Department of Hypertension, Institute of Cardiology, Warsaw, Poland

8. Department of Endocrinology, Diabetology, and Clinical Nutrition, UniversitätsSpital Zürich, Zürich, Switzerland

9. Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands

Publisher

American Medical Association (AMA)

Subject

General Medicine

Reference53 articles.

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2. Development of genome-derived tumor type prediction to inform clinical cancer care.;Penson;JAMA Oncol,2020

3. Artificial intelligence and surgical decision-making.;Loftus;JAMA Surg,2020

4. Trends and focus of machine learning applications for health research.;Beaulie u-Jones;JAMA Netw Open,2019

5. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis.;Seymour;JAMA,2019

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