Health recommender systems: concepts, requirements, technical basics and challenges

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Personalized Medicine

Background:

• Vast amounts of clinical data are collected, but often scattered across different sites.
• Personal Health Record systems (PHRS) aim to centralize patient data but face challenges with expert-oriented language and information overload.
• Patients struggle to comprehend their health status and make informed decisions due to complex medical information.

Purpose of the Study:

• To introduce Health Recommender Systems (HRS) as an enhancement to Personal Health Record systems (PHRS).
• To explain the utility of HRS in providing laymen-friendly information for better patient comprehension of their health status.
• To present basic concepts, scenarios, and an initial implementation of HRS.

Main Methods:

• Discussion of core concepts and use-case scenarios for health recommender systems.
• Presentation of a prototype implementation of a health recommender system.
• Outline of an evaluation approach for HRS, including expert-supported validation and construction of a test collection for case-related recommendations.

Main Results:

• Health recommender systems can bridge the gap between complex medical data and patient understanding.
• A prototype implementation demonstrates the feasibility of adapting recommender systems for the health domain.
• An evaluation framework and test collection are proposed to assess the relevance and effectiveness of HRS.

Conclusions:

• Health recommender systems are a valuable addition to Personal Health Record solutions, empowering patients with understandable health information.
• Tailoring recommender systems to the specific needs of the health domain is crucial for delivering relevant patient information.
• Further research and development are needed to address challenges and optimize the performance of health recommender systems.