Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery.

Journal: The Journal of surgical research

PMID: 32197791

Abstract

BACKGROUND: Reduced surgical site infection (SSI) rates have been reported with use of closed incision negative pressure therapy (ciNPT) in high-risk patients.

Authors

Bora Chang

KelaHealth, Durham, North Carolina. Electronic address: bora@kelahealth.com.
Zhifei Sun

Department of Surgery, Division of Cardiovascular and Thoracic Surgery, Duke University Medical Center, Durham, North Carolina.
Prabath Peiris

KelaHealth, Durham, North Carolina.
Erich S Huang

KelaHealth, Durham, North Carolina; Department of Surgery, Duke University Medical Center, Durham, North Carolina.
Ehsan Benrashid

Department of Surgery, Duke University Medical Center, Durham, North Carolina.
Ellen D Dillavou

Department of Surgery, Duke University Medical Center, Durham, North Carolina.

Keywords

Aged Decision Support Techniques Deep Learning Female Groin Humans Male Middle Aged Negative-Pressure Wound Therapy Retrospective Studies Risk Assessment Vascular Surgical Procedures

External Resources

View on PubMed Access via DOI PubMed (32197791)

Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery.

Abstract

Authors

Keywords

External Resources

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