دورية أكاديمية
A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery.
العنوان: | A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery. |
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المؤلفون: | Jiam ML; School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA., Xin KZ; Department of Radiology, University of California - Irvine, Irvine, California, USA., Ha PK; Department of Otolaryngology - Head & Neck Surgery, University of California - San Francisco, San Francisco, California, USA., Jiam NT; Department of Otolaryngology - Head & Neck Surgery, University of California - San Francisco, San Francisco, California, USA.; Department of Otolaryngology - Head & Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA. |
المصدر: | Head & neck [Head Neck] 2024 May; Vol. 46 (5), pp. 1001-1008. Date of Electronic Publication: 2024 Feb 12. |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: John Wiley And Sons Country of Publication: United States NLM ID: 8902541 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0347 (Electronic) Linking ISSN: 10433074 NLM ISO Abbreviation: Head Neck Subsets: MEDLINE |
أسماء مطبوعة: | Publication: New York, NY : John Wiley And Sons Original Publication: New York, NY : J. Wiley, c1989- |
مواضيع طبية MeSH: | Supervised Machine Learning* , Head and Neck Neoplasms*/surgery, Humans ; Retrospective Studies ; Neck ; Predictive Value of Tests |
مستخلص: | Background: New patient referrals are often processed by practice coordinators with little-to-no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment. The study objective is to develop a supervised machine learning (ML) platform that identifies relevant variables associated with head and neck surgical resection. Methods: A retrospective cohort study was conducted on 64 222 patient datapoints from the SEER database. Results: The random forest ML model correctly classified patients who were offered head and neck surgery with an 81% accuracy rate. The sensitivity and specificity rates were 86% and 71%. The positive and negative predictive values were 85% and 73%. Conclusions: ML modeling accurately predicts head and neck cancer surgery recommendations based on patient and cancer information from a large population-based dataset. ML adjuncts for referral processing may decrease the time to treatment for patients with cancer. (© 2024 Wiley Periodicals LLC.) |
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فهرسة مساهمة: | Keywords: carcinoma; decision tree; head and neck cancer; machine learning; random forest; surgery |
تواريخ الأحداث: | Date Created: 20240212 Date Completed: 20240410 Latest Revision: 20240410 |
رمز التحديث: | 20240410 |
DOI: | 10.1002/hed.27674 |
PMID: | 38344931 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1097-0347 |
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DOI: | 10.1002/hed.27674 |