Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma-Reference-Cited by-同舟云学术

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma

Published:2021-01-21 Issue:7 Volume:31 Page:5434-5441
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Staziaki Pedro Vinícius^ORCID,Wu Di,Rayan Jesse C.,Santo Irene Dixe de Oliveira,Nan Feng,Maybury Aaron,Gangasani Neha,Benador Ilan,Saligrama Venkatesh,Scalera Jonathan,Anderson Stephan W.

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-020-07534-w.pdf

Reference22 articles.

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2. Haider AH, Hashmi ZG, Zafar SN et al (2014) Developing best practices to study trauma outcomes in large databases: an evidence-based approach to determine the best mortality risk adjustment model. J Trauma Acute Care Surg 76:1061–1069

3. Glance LG, Osler T (2001) Beyond the major trauma outcome study: benchmarking performance using a national contemporary, population-based trauma registry. J Trauma 51:725–727

4. Boyd CR, Tolson MA, Copes WSJTJ (1987) Evaluating trauma care: the TRISS method. Trauma Score and the Injury Severity Score. J Trauma 27:370–378

5. Cook A, Osler T, Glance L et al (2018) Comparison of two prognostic models in trauma outcome. Br J Surg 105:513–519

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