Identifying amyotrophic lateral sclerosis through interactions with an internet search engine

Abstract

Introduction/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. Here 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 (ranksum test, p < 0.05 with Bonferroni correction). Mild cognitive impairment could not be distinguished from ALS (p > 0.05). In the prospective analysis, the model reached an AUC of 0.74.DiscussionThese 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 reduce diagnostic delay.

Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis

“…49 The prospective validation further supported the approach's efficacy, indicating its potential as a screening tool to reduce ALS-associated diagnostic delays. 49 These studies highlight the value of harnessing internet search data for early detection of neurodegenerative diseases, and offer promising avenues for improving clinical outcomes.…”

“…Similarly, Yom-Tov et al investigated the potential of internet search engine interactions in identifying individuals with amyotrophic lateral sclerosis (ALS). 49 By analyzing search engine query data, the authors developed a model capable of distinguishing individuals with ALS from controls and disease mimics with high accuracy. 49 The prospective validation further supported the efficacy of this approach, indicating its potential as a screening tool to reduce diagnostic delays associated with ALS.…”

“…49 By analyzing search engine query data, the authors developed a model capable of distinguishing individuals with ALS from controls and disease mimics with high accuracy. 49 The prospective validation further supported the efficacy of this approach, indicating its potential as a screening tool to reduce diagnostic delays associated with ALS. 49 These studies collectively highlight the value of harnessing internet search data for early detection and management of neurodegenerative diseases and offer promising avenues for improving clinical outcomes and patient care.…”