CrowdHealth: um sistema de recomendação de clínicas de saúde num contexto Smart-Health usando crowdsourcing

Abstract

With the emergence of crowdsourcing with the worldwide spread of smartphones recent efforts and important research on the use of crowdsourcing in health or smart-health are intended to assist in improving health habits, construction of historical long-term medical personnel, medical analysis and data review, control diets, stress management, analysis and comparison of information and real-time assistance for disasters. However, none of them used the crowdsourcing for recommendation clinical health centers. In this context, this paper presents a model of clinical health centers recommendation system called CrowdHealth. The main contribution of clinical health centers recommendation system model is possible to create a win-win relationship between its users that can be citizens, doctors or entities linked to the government. In the literature we find some papers that require the use of crowdsourcing as a data source for recommendation clinical health centers approach. We have developed a prototype application based on clinical health centers recommendation system model to provide a vision of what would be an application based on the clinical health centers recommendation system model. To evaluate our model, we present a hypothetical scenario based on a possible application to measure the perception of users and the utility of clinical health centers. The scenarios described took into consideration the following criteria: (1) the distance from the user to the clinical center, (2) the evaluation of other users on the service received in the clinical centers and (3) the time of service reported by users. Thus we performed a simulation of user requests recommendations using a real dataset containing information of Foursquare. The file dataset haved 227428 check in’s in New York City, USA. The file was divided into two parts, where the first represented the textit check in ’s performed by users in clinical centers, and the second represented by requesting users polyclinics recommendations elsewhere. Thus, functions were created to simulate service time calculation and evaluation processes of polyclinics by users. Also users was simulated by ordering polyclinics recommendations elsewhere. So we measure precision and recall of health clinical centers suggested for each user. Average values obtained from 57.5 % and 61.33 % for precision and recall, respectively. Thus, our assessment that portrays clinical health centers recommended by an application based on CrowdHealth could increase beneficially the usefulness of clinical health centers recommended for users.