An Automatic Adaptive Case-based Reasoning System for Depression Remedy Recommendation

Abstract

Social media data represents the fuel for advanced analytics concerning people's behaviors, physiological and health status. These analytics include identifying users' depression levels via Twitter and then recommend remedies. Remedies come in the form of suggesting some accounts to follow, displaying motivational quotes, or even recommending a visit to a psychiatrist. This paper proposes a remedy recommendation system which exploits case-based reasoning (CBR) with random forest. The system recommends the appropriate remedy for a person. The main contribution of this work is the creation of an automated, data-driven, and scalable adaptation module without human interference. The results of every stage of the system were verified by certified psychiatrist. Another contribution of this work is setting the weights in case similarity measurement by the features' importance, extracted from the depression identification system. CBR retrieval accuracy (exact hit) reached 82% while the automatic adaptation accuracy (exact remedy) reached 88%. The adaptation presented an error-tolerance advantage which enhances the overall accuracy.