JMIR Publications

ABSTRACT

Background:

Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at-risk to seek immediate preventive action (e.g. anticoagulants for atrial fibrillation; control of hypertension).

Objective:

Here we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event.

Methods:

We analyzed queries submitted to the Bing search engine by 285 people who identified themselves as having undergone a stroke event and 1195 controls, with regards to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions.

Results:

The model performed well against all comparator groups with a receiver operating curve (ROC) of 0.985 or higher and a true positive rate (at 1% false positive rate) above 80% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data was acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected one year later. The most predictive attributes of the model are associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session.

Conclusions:

The algorithm we propose offers a screening test for a near stroke event. After clinical validation it may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously and on a large scale with the aim of reducing stroke events.