A
Andrés M. Bur
Researcher at University of Kansas
Publications - 72
Citations - 1103
Andrés M. Bur is an academic researcher from University of Kansas. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 15, co-authored 49 publications receiving 620 citations. Previous affiliations of Andrés M. Bur include Columbia University & University of Pennsylvania.
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Journal ArticleDOI
Mental health among otolaryngology resident and attending physicians during the COVID-19 pandemic: National study.
Alyssa M. Civantos,Yasmeen M. Byrnes,Changgee Chang,Aman Prasad,Kevin Chorath,Seerat K. Poonia,Carolyn M. Jenks,Andrés M. Bur,Punam Thakkar,Evan M. Graboyes,Rahul Seth,Samuel Trosman,Anni Wong,Benjamin M. Laitman,Brianna Harris,Janki Shah,Vanessa C. Stubbs,Garret Choby,Qi Long,Christopher H. Rassekh,Erica R. Thaler,Karthik Rajasekaran +21 more
TL;DR: Otolaryngologists are among the highest risk for COVID‐19 exposure, according to a study published in JAMA Oncology.
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Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.
Andrés M. Bur,Andrew J. Holcomb,Sara Goodwin,Janet Woodroof,Omar A. Karadaghy,Yelizaveta Shnayder,Kiran Kakarala,Jason A. Brant,Matthew Shew +8 more
TL;DR: Improved predictive algorithms are needed to ensure that patients with occult nodal disease are adequately treated while avoiding the cost and morbidity of neck dissection in patients without pathologic nodal Disease.
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Artificial Intelligence for the Otolaryngologist: A State of the Art Review.
TL;DR: A state of the art review of artificial intelligence (AI), including its subfields of machine learning and natural language processing, as it applies to otolaryngology and to discuss current applications, future impact, and limitations of these technologies.
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Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.
TL;DR: The developed prediction model proved to be better than a prediction model that exclusively used TNM pathologic and clinical stage according to all performance metrics, and highlights the role that machine learning may play in individual patient risk estimation in the era of big data.
Journal ArticleDOI
Association of Clinical Risk Factors and Postoperative Complications With Unplanned Hospital Readmission After Head and Neck Cancer Surgery
Andrés M. Bur,Jason A. Brant,Carolyn L. Mulvey,Elizabeth A. Nicolli,Robert M. Brody,John P. Fischer,Steven B. Cannady,Jason G. Newman +7 more
TL;DR: This study evaluated clinical factors and postoperative complications to determine which ones were associated with 30-day unplanned hospital readmission after surgery for malignant neoplasms of the head and neck.