Development and validation of a machine learning-based model to assess probability of systemic inflammatory response syndrome in patients with severe multiple traumas.

Journal: BMC medical informatics and decision making
PMID:

Abstract

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a predictor of serious infectious complications, organ failure, and death in patients with severe polytrauma and is one of the reasons for delaying early total surgical treatment. To determine the risk of SIRS within 24 h after hospitalization, we developed six machine learning models.

Authors

  • Alexander Prokazyuk
    University Hospital of Non-Commercial Joint-Stock Company "Semey Medical University", 1a, Ivan Sechenov str, Semey city, 071400, Republic of Kazakhstan. prokazyuk.md@yandex.ru.
  • Aidos Tlemissov
    Center of habilitation and rehabilitation of persons with disabilities of the Abai region, 109, Karagaily, Semey city, 071400, Republic of Kazakhstan.
  • Marat Zhanaspayev
    Non-Commercial Joint-Stock Company "Semey Medical University", 103, Abai Kunanbayev str, Semey city, 071400, Republic of Kazakhstan.
  • Sabina Aubakirova
    Non-Commercial Joint-Stock Company "Semey Medical University", 103, Abai Kunanbayev str, Semey city, 071400, Republic of Kazakhstan.
  • Arman Mussabekov
    Non-Commercial Joint-Stock Company "Semey Medical University", 103, Abai Kunanbayev str, Semey city, 071400, Republic of Kazakhstan.