Novel Technique of Healthcare Record Indexing and Recommendation Based on Trending Queries in Social Media

Abstract

Recommendation of services and applications based on user-data analytics is the most common approach to understanding user requirements. In this article, a novel technique for user recommendation is proposed and validated. The technique uses a Twitter Application Programming Interface (API) handle-based dataset for evaluating and computing the recommendations. The technique uses an open platform Graphical User Interface (GUI) for keyword categorization and building a reliable support system for query analysis. API driven queries from Twitter are cross-validated with labeling techniques and trending hashtags. Typically, the defined tweets are validated to build a Healthcare Record Indexing (HRI) data structure. The HRI is used to support the decision-making and recommendation of services of various healthcare applications and tweets. The technique has trained 750 datasets of categorized clusters with 150 000 tweets (dynamic) datasets from Twitter API. The technique has performed 92.68 % in accuracy and 91.72 % in sensitivity of given datasets.