Screening for cancer using a learning Internet advertising system
Elad Yom-Tov

TL;DR
This study demonstrates that adaptive internet advertising systems combined with clinical questionnaires can effectively identify individuals suspected of having solid tumor cancers, offering a novel screening approach especially useful in less developed healthcare settings.
Contribution
The paper introduces a novel method utilizing adaptive advertising systems and search query data to screen for cancer, showing promising results in real-world settings.
Findings
Classifier achieved 0.64 AUC in predicting cancer suspicion from Bing queries.
Google ads system learned to identify suspected cancer cases with 11% suspected cancer rate.
Search query-based screening can be effective in low-resource healthcare environments.
Abstract
Studies have shown that the traces people leave when browsing the internet may indicate the onset of diseases such as cancer. Here we show that the adaptive engines of advertising systems in conjunction with clinically verified questionnaires can be used to identify people who are suspected of having one of three types of solid tumor cancers. In the first study, 308 people were recruited through ads shown on the Bing search engine to complete a clinically verified risk questionnaire. A classifier trained to predict questionnaire response using only past queries on Bing reached an Area Under the Curve of 0.64 for all three cancer types, verifying that past searches could be used to identify people with suspected cancer. The second study was conducted using the Google ads system in the same configuration as in the first study. However, in this study the ads system was set to…
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