A Random Forest Model Using Flow Cytometry Data Identifies Pulmonary Infection after Thoracic Injury

Gelbard, Rondi B.; Hensman, Hannah; Schobel, Seth; Stempora, Linda; Gann, Eric; Moris, Dimitrios; Dente, Christopher J.; Buchman, Timothy G.; Kirk, Allan D.; Elster, Eric A.

doi:10.1097/ta.0000000000003937

J Trauma Acute Care Surg

2023

DOI: 10.1097/ta.0000000000003937

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A Random Forest Model Using Flow Cytometry Data Identifies Pulmonary Infection after Thoracic Injury

Rondi B. Gelbard

Hannah Hensman

Seth Schobel

et al.

Abstract: The purpose of this study was to evaluate whether assessment of cell phenotypes using flow cytometry (FCM), combined with clinical data, could identify pulmonary infection immediately following thoracic trauma.

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Tissue-resident memory and memory T stem cell subsets may be important predictors of pulmonary infection after thoracic injury rather than T central memory cells

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et al. 2024

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A Random Forest Model Using Flow Cytometry Data Identifies Pulmonary Infection after Thoracic Injury

Abstract: The purpose of this study was to evaluate whether assessment of cell phenotypes using flow cytometry (FCM), combined with clinical data, could identify pulmonary infection immediately following thoracic trauma.

Cited by 2 publications

References 31 publications

Tissue-resident memory and memory T stem cell subsets may be important predictors of pulmonary infection after thoracic injury rather than T central memory cells

Tissue-resident memory and memory T stem cell subsets may be important predictors of pulmonary infection after thoracic injury rather than T central memory cells

Traditional Chinese Manual Therapy (Tuina) Improves Knee Osteoarthritis by Regulating Chondrocyte Autophagy and Apoptosis via the PI3K/AKT/mTOR Pathway: An in vivo Rat Experiment and Machine Learning Study

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