传统机器学习与自动机器学习的比较分析：推进倒置乳头状瘤与相关鳞状细胞癌的诊断。

IF 7.2 2区医学 Q1 OTORHINOLARYNGOLOGY International Forum of Allergy & Rhinology Pub Date : 2024-08-26 DOI:10.1002/alr.23438

Farideh Hosseinzadeh, S Saeed Mohammadi, James N Palmer, Michael A Kohanski, Nithin D Adappa, Michael T Chang, Peter H Hwang, Jayakar V Nayak, Zara M Patel

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引用次数: 0

摘要

要点：倒置乳头状瘤转化为鳞状细胞癌并不总是那么容易预测。与传统的 ML 相比，使用 AutoML 所需的技术知识和技能要少得多。AutoML 在区分 IP 与 IP-SCC 方面超过了传统的 ML 算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis.

Key points: Inverted papilloma conversion to squamous cell carcinoma is not always easy to predict. AutoML requires much less technical knowledge and skill to use than traditional ML. AutoML surpassed the traditional ML algorithm in differentiating IP from IP-SCC.

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来源期刊

International Forum of Allergy & Rhinology OTORHINOLARYNGOLOGY-

CiteScore

11.70

自引率

10.90%

发文量

185

审稿时长

6-12 weeks

期刊介绍： International Forum of Allergy & Rhinologyis a peer-reviewed scientific journal, and the Official Journal of the American Rhinologic Society and the American Academy of Otolaryngic Allergy. International Forum of Allergy Rhinology provides a forum for clinical researchers, basic scientists, clinicians, and others to publish original research and explore controversies in the medical and surgical treatment of patients with otolaryngic allergy, rhinologic, and skull base conditions. The application of current research to the management of otolaryngic allergy, rhinologic, and skull base diseases and the need for further investigation will be highlighted.