Screening for amyotrophic lateral sclerosis through interactions with an internet search engine

Abstract

Amyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with a diagnostic delay of over a year. Here we examine the possibility that interactions with an internet search engine could be used to help screen for ALS. We identified 285 anonymous Bing users whose queries indicated that they had been diagnosed with ALS and matched them to 1) 3276 control users and 2) 1814 users whose searches indicated they had ALS disease mimics. We tested whether the ALS group could be distinguished from controls and disease mimics based on search engine query data. Finally, we conducted a prospective validation from participants who provided access to their Bing search data. The model distinguished between the ALS group and controls with an area under the curve (AUC) of 0.81. Model scores for the ALS group differed from the disease mimics group (ranksum test, P<0.05 with Bonferrini correction). Mild cognitive impairment could not be distinguished from ALS (NS). In the prospective analysis, the model reached an AUC of 0.74. These results suggest that interactions with search engines could be used as a tool to assist in screening for ALS, to reduce diagnostic delay.

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.…”

“…19,[23][24][25][26][27]29,30,38,40 Three studies recruited participants from online research crowdsourcing platforms or advocacy groups. 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…”

“…Japan). 20,21,31,32,[34][35][36]39,41 Investigators then identi ed users who searched for a 'diagnosis ascertaining query' (e.g., 'I have lung cancer', 'I have been diagnosed with diabetes') and extracted their search data from a period of time prior to the diagnosis ascertaining query.…”

“…The consent rate for the use of participant personal search data in studies from individuals providing explicit consent ranged from 20-70%. 25,34 Search engines and extraction Search engine, data extraction, ground truth determination and outcomes from included studies are detailed in Table 2.…”

“…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.…”