@article{yom-tov2020screening,
author = {Yom-Tov, Elad},
title = {Screening for Cancer Using a Learning Internet Advertising System},
year = {2020},
month = {March},
abstract = {Studies have shown that search engine queries are indicative of future diagnosis of several types of cancer. These studies were based on self-identification of illness and were limited in that diagnostic information could not be shared with screened individuals. Here I report on two studies that overcome these limitations.

Advertisements were displayed on the Bing and Google ads systems to people who sought to self-diagnose one of three types of cancer. People who clicked on these ads were provided with clinically verified questionnaires and the outcomes of these questionnaires.

A classifier trained to predict suspected cancer, inferred from questionnaire responses, from past Bing queries reached an area under the curve of 0.64. People who received information that their symptoms were consistent with suspected cancer increased searches for healthcare utilization.

In a second study, questionnaire responses provided to the conversion optimization mechanism of the Google advertisement system enabled it to learn to identify people who were likely to have suspected cancer. Following a training period of approximately 10 days, 11% of people selected for showing of targeted campaign ads were found to have suspected cancer.

These results demonstrate the utility of using modern advertising systems to identify people who are likely suffering from serious medical conditions.},
url = {http://approjects.co.za/?big=en-us/research/publication/screening-for-cancer-using-a-learning-internet-advertising-system/},
journal = {ACM Transactions on Computing for Healthcare},
volume = {1},
number = {2},
}