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| Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA |
| Akash Gupta, Tieming Liu, Scott Shepherd, William Paiva |
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Healthc Inform Res. 2018;24(2):139-147. Published online April 30, 2018 DOI: https://doi.org/10.4258/hir.2018.24.2.139 |
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Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA Healthcare Informatics Research. 2018;24(2):139  Correction: Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA Healthcare Informatics Research. 2018;24(3):250 Prognostic Accuracy of SOFA, qSOFA, and SIRS for Mortality Among Emergency Department Patients with Infections Infection and Drug Resistance. 2021;Volume 14:2763-2775 Time for Sepsis-3 in kids? – Prognostic accuracy of age-adapted SOFA, SIRS, PELOD-2, and qSOFA in children with infection Australian Critical Care. 2018;31(2):120-121 Time for Sepsis-3 in kids? – Prognostic accuracy of age-adapted SOFA, SIRS, PELOD-2, and qSOFA in children with infection Australian Critical Care. 2018;31(2):112-113 Prognostic accuracy of SIRS criteria and qSOFA score for in-hospital mortality among influenza patients in the emergency department . 2020; Prognostic Accuracy of the SIRS, qSOFA, and NEWS for Early Detection of Clinical Deterioration in SARS-CoV-2 Infected Patients Journal of Korean Medical Science. 2020;35(25): Letter to the Editor: Discussion of the Article “Prognostic Accuracy of the SIRS, qSOFA, and NEWS for Early Detection of Clinical Deterioration in SARS-CoV-2 Infected Patients” Journal of Korean Medical Science. 2020;35(30): Prognostic accuracy of SIRS criteria and qSOFA score for in-hospital mortality among influenza patients in the emergency department BMC Infectious Diseases. 2020;20(1): Accuracy of statistical machine learning methods in identifying client behavior patterns at network edge 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). 2019; |
