Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire

Abstract

Background Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists. Objective We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders. Methods Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire. Results A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder. Conclusions Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.

Systematic review of health research using internet search data

“…22,25 A wide range of conditions of interest was examined. Broadly, included conditions encompassed mental health (i.e., schizophrenia spectrum disorder, suicidality, mood disorders, psychosis, eating disorders), 19,22,24,[26][27][28][29][30] neurologic conditions (i.e., Parkinson's disease, amyotrophic lateral sclerosis, stroke, sleeping disorders), [31][32][33][34] malignancies (i.e., pancreatic cancer, lung cancer, paediatric oncology, breast cancer, colon cancer, gynaecological malignancies), 23,25,[35][36][37] miscellaneous conditions (i.e., diabetes, coeliac disease, biliary atresia, pyloric stenosis), 20,21,38,39 and a variety of primary and secondary care presentations. 40,41 Ten studies recruited participants from inpatient or outpatient settings, and consented participants face-to-face.…”

“…22,28,34 Two studies recruited participants with targeted advertisements on search engines. 33,37 Nine studies extracted search data from unique, non-identi able users from back-end databases (8 Bing, 1…”

“…Ten studies used Bing (Microsoft Corporation), 20,[31][32][33][34][35][36][37]39,41 nine studies used Google Search (Google LLC), 19,[24][25][26]26,29,30,38,40 one study used Yahoo! Search Japan (Yahoo Inc.), 21 one study utilised data from both Google and Bing, 23 and two studies used unspeci ed general web browsing history, including search history data.…”

“…15 studies developed machine learning-driven diagnostic or predictive classi ers. [20][21][22][23]27,28,[31][32][33][34][35][36][37][38][39] A variety of machine learning algorithms were used, including decision tree, gradient boosted, linear regression, logistic regression, random forest, and support vector machine. Two studies utilised NLP for categorisation and linguistic thematic analysis of search queries .…”

Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method

“…Tkachenko et al [ 30 ] revealed that Google Trends could detect early signs of diabetes by monitoring combinations of keywords in online search queries. Sleep disorders [ 31 ] and mental health problems [ 32 ] were also found to be related to search volumes.…”