Therapy Decision Support Based on Recommender System Methods
Felix Gräßer, Stefanie Beckert, Denise Küster, Jochen M. Schmitt, Susanne Abraham, Hagen Malberg, Sebastian Zaunseder · Journal of Healthcare Engineering · 2017
We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely,Collaborative RecommenderandDemographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. TheCollaborative Recommenderproves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, theDemographic-based Recommenderperforms worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.