  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 automatically learn to identify people with suspected cancer. A total of
70,586 people were shown the ads, and 6,484 clicked and were referred to
complete the clinical questionnaires. People from countries with higher
Internet access and lower life expectancy tended to click more on the ads. Over
time the advertisement system learned to identify people who were likely to
have symptoms consistent with suspected cancer, such that the percentage of
people completing the questionnaires and found to have suspected cancer reached
approximately 11\% at the end of the experiment.
  These results demonstrate the utility of using search engine queries to
screen for possible cancer and the application of modern advertising systems to
help identify people who are likely suffering from serious medical conditions.
This is especially true in countries where medical services are less developed.
