Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry.

Journal: Clinical chemistry and laboratory medicine
PMID: 33742969

Abstract

OBJECTIVES: Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model.

Authors

  • Gabriel L Streun
    Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
  • Andrea E Steuer
    Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
  • Lars C Ebert
    Department of Forensic Medicine and Imaging, Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, 8057, Zurich, Switzerland. lars.ebert@virtopsy.com.
  • Akos Dobay
    Department of Forensic Genetics, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
  • Thomas Kraemer
    Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.

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