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

Streun, Gabriel L; Steuer, Andrea E; Ebert, Lars C; Dobay, Akos; Kraemer, Thomas

Streun, Gabriel L; Steuer, Andrea E; Ebert, Lars C; Dobay, Akos; Kraemer, Thomas.

Afiliação

Streun GL; Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
Steuer AE; Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
Ebert LC; Department of Forensic Imaging/Virtopsy, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
Dobay A; Department of Forensic Imaging/Virtopsy, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
Kraemer T; Department of Forensic Genetics, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.

Clin Chem Lab Med ; 59(8): 1392-1399, 2021 07 27.

Article em En | MEDLINE | ID: mdl-33742969

Assuntos

Aprendizado de Máquina; Preparações Farmacêuticas; Cromatografia Líquida; Humanos; Espectrometria de Massas; Detecção do Abuso de Substâncias

Palavras-chave

artificial neural networks; high-resolution mass spectrometry; machine learning; progenesis QI; sample manipulation; urine adulteration

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Preparações Farmacêuticas / Aprendizado de Máquina Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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