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摘要:
BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.
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篇名 Artificial intelligence in dentistry:Harnessing big data to predict oral cancer survival
来源期刊 世界临床肿瘤学杂志:英文版 学科 医学
关键词 Oral cancer survival Machine learning Artificial intelligence Dental medicine Public health Surveillance Epidemiology and End Results Quality of life
年,卷(期) 2020,(11) 所属期刊栏目
研究方向 页码范围 918-934
页数 17页 分类号 R73
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节点文献
Oral
cancer
survival
Machine
learning
Artificial
intelligence
Dental
medicine
Public
health
Surveillance
Epidemiology
and
End
Results
Quality
of
life
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研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
世界临床肿瘤学杂志:英文版
不定期
2218-4333
北京市朝阳区东四环中路62号楼远洋国际中
出版文献量(篇)
44
总下载数(次)
0
总被引数(次)
0
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