Power to the patients: The HealthNetsocial network. (November 2017)

• Record Type: Journal Article
• Authors: Narducci, Fedelucio; Lops, Pasquale; Semeraro, Giovanni
• Is Part Of: Information systems. Volume 71(2017)
• Page Start: 111
• Page End: 122
• Publication Date: 2017-11
• Subjects: Health recommender systems -- E-health -- Health social network; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2017.07.005
• Languages: English
• ISSNs: 0306-4379

Abstract

Highlights: We defined a social network that helps patients to make more informed choices related to their health status. We implemented a component that supports patients to identify the most relevant medical areas for their health problems. We implemented a recommender system that facilitates patients to find a doctor, a health facility, or a hospital for treating their health problems. We performed experiments whose results show the effectiveness of the approaches. Abstract: HealthNet (HN) is a social network that brings together patients with similar health conditions. HN helps users in finding a solution to their health problems by suggesting doctors and health facilities that best fit the patient profile. Indeed, the core component of HN is a recommender system that suggests patients similar to the target user and supports the choice of the doctor and the hospital for a specific condition. The recommendation algorithm first computes similarities among patients, and then generates a ranked list of doctors and hospitals for a given patient profile by exploiting health data shared by the community. The HN typical user can find the most similar patients, can look how they treated their diseases, and can receive suggestions for solving her condition. In order to facilitate the interaction with the system and improve the recommendation step, the patient can express her health status by a natural-language sentence. The system analyzes the sentence and identifies the most relevant medical area (e.g., orthopedics, neurology, allergology, etc.) for that specific case, and uses this information for the recommendation task. Currently HN is in alpha version and only for Italian users, but in the future we want to extend the platform to other languages. We carried out both an in-vitro experimental evaluation to assess the effectiveness of the module for analyzing natural language descriptions provided by users as well as the recommender system to suggest the right doctors for a specific health problem, and an in-vivo evaluation performed by real doctors. Results are really encouraging.