A Framework Of Clinical Recommender System With Genomic Information

Clinicians make decisions that affect life and death, quality of life, every single day. It is important to support clinicians by discovering medical knowledge from the accumulated electronic health records (EHRs). The integration of genomic information and EHRs are long recognized by the medical community as the inherent feature of the disease. The demand for developing a clinical recommender system that is able to deal with both genomic and phenotypic data is urgent. This paper proposes a framework of clinical recommender system with genomic information, which is used in the clinical process and connects the four types of users: clinicians, patients, clinical labs, researchers. With models and methods in artificial intelligence (AI), five functions are designed in this framework: diagnosis prediction, disease risk prediction, test prediction, and event prediction. The proposed framework will help clinicians to make decisions on the next step in clinical care action for patients.