COGNET-AMO: Personalized Prescription Recommendation using Attention over Medical Order Information

In the context of patients with complex conditions, the capacity to assist physicians in making appropriate prescribing decisions is of paramount importance. The field of prescription recommendation has attracted a growing body of research interest, given its significant clinical value. However, existing research has not taken into account the patients’ intention to visit the doctor and the similarities between patients’ visit intentions. In this study, we employ medical order data in both prescription intention modeling and patient similarity analysis, subsequently integrating these to generate more personalized and accurate prescription recommendations. The cross-attention mechanism is first employed to focus the representation of the patient’s health status on the patient’s chief complaints, with the objective of predicting treatment intention and, subsequently, the prescriptions. Subsequently, when we learn from the successful treatment experiences of similar patients, we focus more on the similarities of their chief complaints. The experimental results on the MIMIC-III dataset demonstrate that our method achieves optimal performance in terms of Jaccard score, PRAUC, F1 score and average precision, with relative improvements of 7%, 5%, 4%, and 8% respectively, in comparison to state-of-the-art approaches.