MedAI+: Intelligent Personalized Healthcare Through Advanced AI Analytics

Personalized healthcare systems become more significant to address the heterogeneous demands of patients, attempting to provide individualized treatment plans according to distinct health conditions. Conventional healthcare recommendation systems do not consider the complexities in a patient’s changing health status and the dynamic interdependence between diseases, treatments, and patient factors. Existing solutions have been symptom-based treatment or co-occurrence relations between disease and medication, which do not express the direct, causal impacts of treatments on a specific health condition of a patient. To overcome these shortcomings, we introduce MedAi, an AI-based healthcare recommendation system for the improvement of personalization through causal modeling. MedAi leverages causal inference methods to express the dynamic interdependencies between diseases, procedures, and treatments across various patient health states, allowing for better accuracy and personalized treatment recommendation. The essence of MedAi is to examine longitudinal patient data to express the change of a patient’s health over time, considering the intricate causal interrelations among medical entities, and finally, to provide personalized medication recommendations. We performed thorough experiments on real-world data to demonstrate that MedAi is superior to existing healthcare recommendation models since it makes more precise and safer treatment predictions. This paper describes how MedAi was created and operated, the limitations in healthcare recommendation systems, and the significant improvements of applying casual-based state modeling.