Graph neural collaborative filtering with medical content-aware pre-training for treatment pattern recommendation

Recently, considering the advancement of information technology in healthcare, electronic medical records (EMRs) have become the repository of patients' treatment processes in hospitals, including the patient's treatment pattern (standard treatment process), the patient's medical history, the patient's admission diagnosis, etc. In particular, EMRs-based treatment recommendation systems have become critical for optimizing clinical decision-making. EMRs contain complex relationships between patients and treatment patterns. Recent studies have shown that graph neural collaborative filtering can effectively capture the complex relationships in EMRs. However, none of the existing methods take into account the impact of medical content such as the patient's admission diagnosis, and medical history on treatment recommendations. In this work, we propose a graph neural collaborative filtering model with medical content-aware pre-training (CAPRec) for learning initial embeddings with medical content to improve recommendation performance. First the model constructs a patient-treatment pattern interaction graph from EMRs data. Then we attempt to use the medical content for pre-training learning and transfer the learned embeddings to a graph neural collaborative filtering model. Finally, the learned initial embedding can support the downstream task of graph collaborative filtering. Extensive experiments on real world datasets have consistently demonstrated the effectiveness of the medical content-aware training framework in improving treatment recommendations.