Article ; Online: A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery.
2024 Volume 46, Issue 5, Page(s) 1001–1008
Abstract: 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 ...
Abstract | 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. |
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MeSH term(s) | Humans ; Retrospective Studies ; Supervised Machine Learning ; Neck ; Predictive Value of Tests ; Head and Neck Neoplasms/surgery |
Language | English |
Publishing date | 2024-02-12 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 645165-2 |
ISSN | 1097-0347 ; 0148-6403 ; 1043-3074 |
ISSN (online) | 1097-0347 |
ISSN | 0148-6403 ; 1043-3074 |
DOI | 10.1002/hed.27674 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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