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Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.
Kamaleswaran, Rishikesan; Akbilgic, Oguz; Hallman, Madhura A; West, Alina N; Davis, Robert L; Shah, Samir H.
Affiliation
  • Kamaleswaran R; Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
  • Akbilgic O; Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
  • Hallman MA; Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
  • West AN; Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
  • Davis RL; Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
  • Shah SH; Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
Pediatr Crit Care Med ; 19(10): e495-e503, 2018 10.
Article in En | MEDLINE | ID: mdl-30052552
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sepsis / Machine Learning Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Adolescent / Child / Female / Humans / Male Language: En Journal: Pediatr Crit Care Med Journal subject: PEDIATRIA / TERAPIA INTENSIVA Year: 2018 Document type: Article Affiliation country: Tunisia Country of publication: United States
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sepsis / Machine Learning Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Adolescent / Child / Female / Humans / Male Language: En Journal: Pediatr Crit Care Med Journal subject: PEDIATRIA / TERAPIA INTENSIVA Year: 2018 Document type: Article Affiliation country: Tunisia Country of publication: United States