Machine Learning Predicts 30‐Day Readmission and Mortality After Surgical Resection of Head and Neck Cancer

Machine learning models can accurately predict mortality and readmission risks, offering insights into the most influential factors. With further validation, these models may enhance clinical decision-making in postsurgical care for HNSCC patients.

Authors

Ghayoour S. Mir (ORCID: https://orcid.org/0000-0003-3713-6772)
Aman M. Patel (ORCID: https://orcid.org/0000-0003-0794-041X)
Lucy Revercomb (ORCID: https://orcid.org/0009-0002-9996-8240)
Andrey Filimonov (ORCID: https://orcid.org/0000-0002-4285-5862)
Dan Fu (ORCID: https://orcid.org/0000-0003-2762-3841)

Institutions

Rutgers, The State University of New Jersey (US)
Rutgers New Jersey Medical School (US)

Publication Details

Journal: OTO Open
Published: 2025-01-01
DOI: https://doi.org/10.1002/oto2.70100
Citations: 1
Primary Topic: Head and Neck Cancer Studies
Type: article
Field-Weighted Citation Impact: 1.85

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article

Machine Learning Predicts 30‐Day Readmission and Mortality After Surgical Resection of Head and Neck Cancer

Ghayoour S. Mir, Aman M. Patel, Lucy Revercomb, Andrey Filimonov et al.

Jan 1, 2025

1 citations

OTO Open

Head and Neck Cancer Studies

1.85

article

Machine Learning Predicts 30‐Day Readmission and Mortality After Surgical Resection of Head and Neck Cancer

Ghayoour S. Mir, Aman M. Patel, Lucy Revercomb, Andrey Filimonov, Dan Fu

January 1, 2025article en

1 citations

Abstract

Machine learning models can accurately predict mortality and readmission risks, offering insights into the most influential factors. With further validation, these models may enhance clinical decision-making in postsurgical care for HNSCC patients.

OTO OpenVol. 9(1)

Rutgers, The State University of New Jersey (US), Rutgers New Jersey Medical School (US)

Good health and well-being

Openalex Percentile: Top 18%

Head and Neck Cancer Studies

1.85

DOI Open Access PDF OpenAlex

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