Identifying amyotrophic lateral sclerosis through interactions with an internet search engine

Abstract

AbstractIntroduction/AimsAmyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with an average time from onset of symptoms to diagnosis of about 1 year. Herein we examine the possibility that interactions with an internet search engine can identify people with ALS.MethodsWe 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.ResultsThe 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 (rank sum test, p < .05 with Bonferroni correction). Mild cognitive impairment could not be distinguished from ALS (p > .05). In the prospective analysis, the model reached an AUC of 0.74.DiscussionOur results suggest that interactions with search engines should be further studied to understand the potential to act as a tool to assist in screening for ALS and to reduce diagnostic delay.