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.) |
| References: | Khorana AA, Tullio K, Elson P, et al. Time to initial cancer treatment in the United States and association with survival over time: an observational study. PloS One. 2019;14(3):e0213209. Cone EB, Marchese M, Paciotti M, et al. Assessment of time‐to‐treatment initiation and survival in a cohort of patients with common cancers. JAMA Netw Open. 2020;3(12):e2030072. Patel MP, Schettini P, O'Leary CP, Bosworth HB, Anderson JB, Shah KP. Closing the referral loop: an analysis of primary care referrals to specialists in a large health system. J Gen Intern Med. 2018;33(5):715‐721. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920‐1930. Banerjee M, Reynolds E, Andersson HB, Nallamothu BK. Tree‐based analysis. Circ Cardiovasc Qual Outcomes. 2019;12(5):e004879. Doll KM, Rademaker A, Sosa JA. Practical guide to surgical data sets: Surveillance, Epidemiology, and End Results (SEER) database. JAMA Surg. 2018;153(6):588‐589. Breiman L. Random forests. Mach Learn. 2001;45(1):5‐32. Touw WG, Bayjanov JR, Overmars L, et al. Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? Brief Bioinform. 2013;14(3):315‐326. Karpathiou G, Giroult JB, Forest F, et al. Clinical and histologic predictive factors of response to induction chemotherapy in head and neck squamous cell carcinoma. Am J Clin Pathol. 2016;146(5):546‐553. Johnson DE, Burtness B, Leemans CR, Lui VWY, Bauman JE, Grandis JR. Head and neck squamous cell carcinoma. Nat Rev Dis Primers. 2020;6(1):92. |
| 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 |
Full Text Finder | تدمد: | 1097-0347 |
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| DOI: | 10.1002/hed.27674 |