...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>PLoS Computational Biology >Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling
【24h】

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling

机译：通过组合机器学习和反向贝叶斯建模估算培养人乳头瘤病毒相关口咽癌的条件概率

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries worldwide represents a significant public health concern. Although gender neutral HPV vaccination programmes are expected to cause a reduction in the incidence rates of OPSCCs, these effects will not be evident in the foreseeable future. Secondary prevention strategies are currently not feasible due to an incomplete understanding of the natural history of oral HPV infections in OPSCCs. The key parameters that govern natural history models remain largely ill-defined for HPV related OPSCCs and cannot be easily inferred from experimental data. Mathematical models have been used to estimate some of these ill-defined parameters in cervical cancer, another HPV related cancer leading to successful implementation of cancer prevention strategies. We outline a “double-Bayesian” mathematical modelling approach, whereby, a Bayesian machine learning model first estimates the probability of an individual having an oral HPV infection, given OPSCC and other covariate information. The model is then inverted using Bayes’ theorem to reverse the probability relationship. We use data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry, SEER Head and Neck with HPV Database and the National Health and Nutrition Examination Surveys (NHANES), representing the adult population in the United States to derive our model. The model contains 8,106 OPSCC patients of which 73.0% had an oral HPV infection. When stratified by age, sex, marital status and race/ethnicity, the model estimated a higher conditional probability for developing OPSCCs given an oral HPV infection in non-Hispanic White males and females compared to other races/ethnicities. The proposed Bayesian model represents a proof-of-concept of a natural history model of HPV driven OPSCCs and outlines a strategy for estimating the conditional probability of an individual’s risk of developing OPSCC following an oral HPV infection.
机译：全球几个国家的人乳头瘤病毒（HPV）相关口咽鳞状细胞癌（OPSCC）的流行病率增加了重要的公共卫生问题。虽然预计性别中性HPV疫苗接种计划预计会导致OPSCC的发病率降低，但在可预见的未来，这些效果不会明显。由于对OPSCCS中口服HPV感染的自然病史不完全了解，二级预防策略目前不可行。管理自然历史模型的关键参数仍然很大程度上为HPV相关的OPSCCS而定义，并且不能从实验数据中容易地推断出来。数学模型已被用于估算宫颈癌中的一些不含癌症的参数，另一种HPV相关癌症导致成功实施癌症预防策略。我们概述了一个“双贝叶斯”数学建模方法，由此，贝叶斯机器学习模型首先估计具有口腔HPV感染的个体的概率，给予OPSCC和其他协变量信息。然后使用贝叶斯定理反转模型来反转概率关系。我们使用来自监测，流行病学和最终结果（SEER）癌症登记处，Seer Head和Neck以及HPV数据库以及国家健康和营养考试调查（NHANES）的数据，代表美国的成年人派对我们的模型。该模型包含8,106名OPSCC患​​者，其中73.0％有口服HPV感染。当按年龄，性别，婚姻状况和种族/种族分类时，该模型估计了在与其他种族/种族相比，在非西班牙裔男性男性和女性中赋予OPSCCS的较高的有条件概率。所提出的贝叶斯模型代表了HPV驱动OPSCCS的自然历史模型的概念验证，并概述了估算个人在口服HPV感染后发育OPSCC风险的条件概率的策略。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号