Abstract: This system provides data-driven therapy recommendation for the patients. Therapy will be recommended to a patient by analysing the response from previous records which are similar to the given patient’s record. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, were 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. Both methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves 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, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system. In addition to the above proposed system, new Model-based Recommender is proposed and it is compared with the above system to check its efficiency. Model-based Recommender is also proposed to enhance the efficiency of recommendation. Data mining brings the concept of artificial intelligence, data structures, statistics, and database together. It is a high demand area because many organizations and businesses can benefit from it. The large volume of daily captured data in healthcare institutions and out-of-hospital settings opens up new perspectives for Data Mining in healthcare. Due to the amount of the data, its high dimensionality and complex interdependencies within the data, an efficient integration of the available information is only possible using technical aids. So, data-driven Clinical Decision Support Systems (CDSS) are designated to assist physicians or other health professionals during clinical decision-making.
Keywords: Therapy Recommendation, Hybrid Recommender System, Collaborative Filtering, Demography Based Filtering, Model Based Recommender, Clinical Decision Support System
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DOI:
10.17148/IJARCCE.2019.8118
