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Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study.
Mahdavi, Mahdi; Choubdar, Hadi; Rostami, Zahra; Niroomand, Behnaz; Levine, Alexandra T; Fatemi, Alireza; Bolhasani, Ehsan; Vahabie, Abdol-Hossein; Lomber, Stephen G; Merrikhi, Yaser.
Afiliación
  • Mahdavi M; Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Choubdar H; Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, H3G1Y6, Canada.
  • Rostami Z; Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Niroomand B; Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, H3G1Y6, Canada.
  • Levine AT; Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Fatemi A; Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Bolhasani E; Department of Psychology, University of Western Ontario, London, Ontario, N6A 3K7, Canada.
  • Vahabie AH; Department of Internal Medicine, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Lomber SG; Department of Physics, University of Isfahan, Isfahan, 81746-73441, Iran.
  • Merrikhi Y; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Sci Rep ; 13(1): 2827, 2023 02 17.
Article en En | MEDLINE | ID: mdl-36808151
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pandemias / COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pandemias / COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido