Text Categorization Models for Identifying Unproven Cancer Treatments on the Web
Text Categorization Models for Identifying Unproven Cancer Treatments on the Web
复制标题
用于识别网络上未经证实的癌症治疗的文本分类模型
DOI：
10.3233/978-1-58603-774-1-968
复制
发表时间：
2007
影响因子：
--
通讯作者：
C. Aliferis
中科院分区：
文献类型：
--
作者：
Yindalon Aphinyanagphongs;C. Aliferis
互联网作为一种未经同行评审（且基本上不受监管）的出版媒体的性质，使得不准确和未经证实的医疗主张以前所未有的规模得到广泛宣传。目前病情无法完全治愈的患者特别容易受到未经证实的、危险的奇迹疗法承诺的影响。在极端情况下，已记录了致命的不良后果。最常见的代价是经济、心理和延迟应用不完善但经过验证的科学方法。为了帮助保护可能病入膏肓并容易受到剥削的患者，我们探索了使用机器学习技术来识别未经证实的声明的网页。这项可行性研究表明，所得模型可以全自动识别未经证实的声明的网页，并且比以前的网络工具和最先进的搜索引擎技术要好得多。
The nature of the internet as a non-peer-reviewed (and largely unregulated) publication medium has allowed wide-spread promotion of inaccurate and unproven medical claims in unprecedented scale. Patients with conditions that are not currently fully treatable are particularly susceptible to unproven and dangerous promises about miracle treatments. In extreme cases, fatal adverse outcomes have been documented. Most commonly, the cost is financial, psychological, and delayed application of imperfect but proven scientific modalities. To help protect patients, who may be desperately ill and thus prone to exploitation, we explored the use of machine learning techniques to identify web pages that make unproven claims. This feasibility study shows that the resulting models can identify web pages that make unproven claims in a fully automatic manner, and substantially better than previous web tools and state-of-the-art search engine technology.
DOI：
10.1016/j.ijmedinf.2005.02.002
发表时间：
2005-08-01
影响因子：
4.9
作者：
Bernstam, EV;Sagaram, S;Meric-Bernstam, F
通讯作者：
Meric-Bernstam, F
影响因子：
45.3
作者：
Richardson, MA;Sanders, T;Singletary, SE
通讯作者：
Singletary, SE