Automated Characterization of Mobile Health Apps' Features by Extracting Information from the Web: An Exploratory Study

Purpose: To test the viability of a novel method for automated characterization of mobile health apps. Method: In this exploratory study, we developed the basic modules of an automated method, based on text analytics, able to characterize the apps' medical specialties by extracting information from the Web. We analyzed apps in the Medical (M) and Health & Fitness (H&F) categories on the US iTunes store. Results: We automatically crawled 42007 M and 79557 H&F apps' webpages. After removing duplicates and non-English apps, the database included 80490 apps. We tested the accuracy of the automated method on a subset of 400 apps. We observed 91% accuracy for the identification of apps related to health or medicine, 95% accuracy for Sensory Systems apps, and an average 82% accuracy for classification into medical specialties. Conclusions: These preliminary results suggested the viability of automated characterization of apps based on text analytics and highlighted directions for improvement in terms of: classification rules and vocabularies, analysis of semantic types, and extraction of key features (promoters, services, and users). The availability of automated tools for app characterization is important as it may support healthcare professionals in informed, aware selection of health apps to recommend to their patients.