Yom-Tov Elad, Borsa Diana, Hayward Andrew C, McKendry Rachel A, Cox Ingemar J
Microsoft Research, Herzeliya, Israel.
J Med Internet Res. 2015 Jan 27;17(1):e29. doi: 10.2196/jmir.4082.
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.
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.
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.
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).
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.
全球医疗保健成本的不断攀升推动了新技术的发展，以识别个体疾病风险的早期指标。传统上，流行病学家使用医学数据库和冗长的临床研究来确定此类风险因素，但这些研究往往规模有限且成本高昂，并且可能无法充分考虑存在社会污名的疾病，也无法识别短暂的急性风险因素。
在此我们报告，网络搜索引擎查询与维基百科访问模式信息相结合，可用于推断与个体用户相关的健康事件，并自动为全球一些常见疾病生成基于网络的风险标志物，从心血管疾病到性传播感染、心理健康状况以及妊娠。
使用匿名数据集，我们提出了一些方法，首先区分可能经历过特定健康事件的个体，并将他们分类到不同类别中。然后，我们使用自我对照病例系列方法，在用户搜索查询类别之后的风险期内，直接找出健康事件的发生率，并与同一人群在其他时期的发生率进行比较。
搜索宠物店是过敏的风险标志物。我们还识别出了一些可能的新风险标志物；例如：搜索快餐店和主题餐厅与心肌梗死风险的短暂增加有关，这表明这种接触不仅是一个长期风险因素，还可能是心肌梗死的急性触发因素。约会和成人内容网站是性传播感染（如人类免疫缺陷病毒（HIV））的风险标志物。
基于网络的方法提供了一种强大、低成本的途径来自动识别风险因素，并支持更及时、个性化的公共卫生努力，从而带来人类和经济效益。
