头颈癌临床诊断中的机器学习

IF 1.7 4区 医学 Q2 OTORHINOLARYNGOLOGY
Hollie Black, David Young, Alexander Rogers, Jenny Montgomery
{"title":"头颈癌临床诊断中的机器学习","authors":"Hollie Black,&nbsp;David Young,&nbsp;Alexander Rogers,&nbsp;Jenny Montgomery","doi":"10.1111/coa.14220","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.</p>\n </section>\n \n <section>\n \n <h3> Design</h3>\n \n <p>An observational cohort study.</p>\n </section>\n \n <section>\n \n <h3> Setting</h3>\n \n <p>Queen Elizabeth University Hospital.</p>\n </section>\n \n <section>\n \n <h3> Participants</h3>\n \n <p>Patients who were referred via the USOC pathway between January 2019 and May 2021.</p>\n </section>\n \n <section>\n \n <h3> Main Outcome Measures</h3>\n \n <p>Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Further studies should aim to collect larger samples of malignant and pre-malignant patients to improve the class imbalance and increase the performance of the machine learning models.</p>\n </section>\n </div>","PeriodicalId":10431,"journal":{"name":"Clinical Otolaryngology","volume":"50 1","pages":"31-38"},"PeriodicalIF":1.7000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coa.14220","citationCount":"0","resultStr":"{\"title\":\"Machine Learning in Clinical Diagnosis of Head and Neck Cancer\",\"authors\":\"Hollie Black,&nbsp;David Young,&nbsp;Alexander Rogers,&nbsp;Jenny Montgomery\",\"doi\":\"10.1111/coa.14220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Design</h3>\\n \\n <p>An observational cohort study.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Setting</h3>\\n \\n <p>Queen Elizabeth University Hospital.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Participants</h3>\\n \\n <p>Patients who were referred via the USOC pathway between January 2019 and May 2021.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Main Outcome Measures</h3>\\n \\n <p>Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>Further studies should aim to collect larger samples of malignant and pre-malignant patients to improve the class imbalance and increase the performance of the machine learning models.</p>\\n </section>\\n </div>\",\"PeriodicalId\":10431,\"journal\":{\"name\":\"Clinical Otolaryngology\",\"volume\":\"50 1\",\"pages\":\"31-38\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coa.14220\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Otolaryngology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/coa.14220\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OTORHINOLARYNGOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Otolaryngology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/coa.14220","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

目标机器学习在其他医学领域非常有效,本研究旨在对HNC进行研究,并确定哪种算法最适合对恶性患者进行分类.设计观察性队列研究.设置伊丽莎白女王大学医院.参与者2019年1月至2021年5月期间通过USOC路径转诊的患者.主要结果测量利用人口统计学和症状数据,预测良性、潜在恶性和恶性三类患者的诊断结果经典统计方法序数逻辑回归在数据上效果最佳,AUC 为 0.6697,平衡准确率为 0.641。描述娱乐性吸毒史和生活状况的人口统计学特征是最重要的变量,同时也是颈部肿块这一红色标志症状的重要变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning in Clinical Diagnosis of Head and Neck Cancer

Objective

Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.

Design

An observational cohort study.

Setting

Queen Elizabeth University Hospital.

Participants

Patients who were referred via the USOC pathway between January 2019 and May 2021.

Main Outcome Measures

Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.

Results

The classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.

Conclusion

Further studies should aim to collect larger samples of malignant and pre-malignant patients to improve the class imbalance and increase the performance of the machine learning models.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Clinical Otolaryngology
Clinical Otolaryngology 医学-耳鼻喉科学
CiteScore
4.00
自引率
4.80%
发文量
106
审稿时长
>12 weeks
期刊介绍: Clinical Otolaryngology is a bimonthly journal devoted to clinically-oriented research papers of the highest scientific standards dealing with: current otorhinolaryngological practice audiology, otology, balance, rhinology, larynx, voice and paediatric ORL head and neck oncology head and neck plastic and reconstructive surgery continuing medical education and ORL training The emphasis is on high quality new work in the clinical field and on fresh, original research. Each issue begins with an editorial expressing the personal opinions of an individual with a particular knowledge of a chosen subject. The main body of each issue is then devoted to original papers carrying important results for those working in the field. In addition, topical review articles are published discussing a particular subject in depth, including not only the opinions of the author but also any controversies surrounding the subject. • Negative/null results In order for research to advance, negative results, which often make a valuable contribution to the field, should be published. However, articles containing negative or null results are frequently not considered for publication or rejected by journals. We welcome papers of this kind, where appropriate and valid power calculations are included that give confidence that a negative result can be relied upon.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信