title: Automatic identification of Web-based risk markers for health events.
abstract: BACKGROUND
  The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual's risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors.
  
  
  OBJECTIVE
  Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy.
  
  
  METHODS
  Using anonymized datasets, we present methods to first distinguish individuals likely to have experienced specific health events, and classify them into distinct categories. We then use the self-controlled case series method to find the incidence of health events in risk periods directly following a user's search for a query category, and compare to the incidence during other periods for the same individuals.
  
  
  RESULTS
  Searches for pet stores were risk markers for allergy. We also identified some possible new risk markers; for example: searching for fast food and theme restaurants was associated with a transient increase in risk of myocardial infarction, suggesting this exposure goes beyond a long-term risk factor but may also act as an acute trigger of myocardial infarction. Dating and adult content websites were risk markers for sexually transmitted infections, such as human immunodeficiency virus (HIV).
  
  
  CONCLUSIONS
  Web-based methods provide a powerful, low-cost approach to automatically identify risk factors, and support more timely and personalized public health efforts to bring human and economic benefits.
authors.affiliation: Microsoft Research, Herzeliya, Israel. eladyt@microsoft.com.
authors.forename: Elad
authors.identifier: http://orcid.org/0000-0002-2380-4584
authors.initials: E
authors.lastname: Yom-Tov
authors.forename: Diana
authors.identifier: http://orcid.org/0000-0002-3605-8840
authors.initials: D
authors.lastname: Borsa
authors.forename: Andrew C
authors.identifier: http://orcid.org/0000-0002-3549-6232
authors.initials: AC
authors.lastname: Hayward
authors.forename: Rachel A
authors.identifier: http://orcid.org/0000-0003-2018-6829
authors.initials: RA
authors.lastname: McKendry
authors.forename: Ingemar J
authors.identifier: http://orcid.org/0000-0002-6662-417X
authors.initials: IJ
authors.lastname: Cox
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data*; D020407:Internet; D008297:Male; D011247:Pregnancy; D011634:Public Health; D012307:Risk Factors; D057386:Search Engine; D060388:Support Vector Machine
keywords: Information retrieval query processing; Machine Learning; Web search engines; epidemiology; self-controlled case series