Alessia Paglialonga1, Massimo Schiavo2, Enrico Gianluca Caiani1,2. 1. Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Milan, Italy. 2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy.
Abstract
PURPOSE: The aim of this study was 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 and Health & Fitness categories on the U.S. iTunes store. RESULTS: We automatically crawled 42,007 Medical and 79,557 Health & Fitness apps' webpages. After removing duplicates and non-English apps, the database included 80,490 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 of 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 health care professionals in informed, aware selection of health apps to recommend to their patients.
PURPOSE: The aim of this study was 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 and Health & Fitness categories on the U.S. iTunes store. RESULTS: We automatically crawled 42,007 Medical and 79,557 Health & Fitness apps' webpages. After removing duplicates and non-English apps, the database included 80,490 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 of 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 health care professionals in informed, aware selection of health apps to recommend to their patients.
Authors: Guillermo Molina Recio; Laura García-Hernández; Rafael Molina Luque; Lorenzo Salas-Morera Journal: Biomed Eng Online Date: 2016-07-15 Impact factor: 2.819