KA-Recsys: Patient Focused Knowledge Appropriate Health Recommender System
Authors: Khushboo Thaker
Venue: SIGIR
Year: 2022

Chronic disease patients, such as diabetics, cancer patients, and heart disease patients, actively seek health information for self-management and decision-making every single day. Patient focused health recommender systems (PHRSs) that suggest health information relevant to patients' changing needs, assists them with easy information accessibility. Nevertheless, patients' needs become more complex with disease progression and their increased knowledge about disease. Hence, a unique requirement of the PHRS would be to suggest health information in line with patients' changing knowledge about the disease. However, current PHRS are personalized to patient interest and don't consider their knowledge about disease. By providing patients with information tailored at their knowledge-level, they not only are more likely to understand and engage better in disease management, but can use PHRS for disease related learning. Hence, the overarching goal of my PhD thesis is to explore technologies in the field of recommender systems and personalized learning for the purpose of suggesting health information that accounts for patients' dynamic information needs and level of knowledge about disease. We will explore these ideas in the context of developing a knowledge-appropriate PHRS (KA-PHRS ). A critical innovation of KA-PHRS is the patient knowledge model that keeps track of patients' changing knowledge-level about disease and enables knowledge-appropriate recommendations. The expectation is that health information suggested by KA-PHRS will increase as well as benefit patients' involvement in self management and treatment.Chronic disease patients, such as diabetics, cancer patients, and heart disease patients, actively seek health information for self-management and decision-making every single day. Patient focused health recommender systems (PHRSs) that suggest health information relevant to patients' changing needs, assists them with easy information accessibility. Nevertheless, patients' needs become more complex with disease progression and their increased knowledge about disease. Hence, a unique requirement of the PHRS would be to suggest health information in line with patients' changing knowledge about the disease. However, current PHRS are personalized to patient interest and don't consider their knowledge about disease. By providing patients with information tailored at their knowledge-level, they not only are more likely to understand and engage better in disease management, but can use PHRS for disease related learning. Hence, the overarching goal of my PhD thesis is to explore technologies in the field of recommender systems and personalized learning for the purpose of suggesting health information that accounts for patients' dynamic information needs and level of knowledge about disease. We will explore these ideas in the context of developing a knowledge-appropriate PHRS (KA-PHRS ). A critical innovation of KA-PHRS is the patient knowledge model that keeps track of patients' changing knowledge-level about disease and enables knowledge-appropriate recommendations. The expectation is that health information suggested by KA-PHRS will increase as well as benefit patients' involvement in self management and treatment.