PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records
This work addresses the need for personalized medication recommendations for doctors using EHR data, but it is incremental as it builds on existing recommender technologies with attention mechanisms.
The authors tackled the problem of personalized medication recommendation from electronic health records by developing PREMIER, a two-stage attention-based system that minimizes adverse drug interactions, and it outperformed state-of-the-art methods on MIMIC-III and a proprietary dataset while balancing accuracy and drug-drug interaction tradeoffs.
The broad adoption of Electronic Health Records (EHR) has led to vast amounts of data being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. In this work, we design a two-stage attention-based personalized medication recommender system called PREMIER which incorporates information from the EHR to suggest a set of medications. Our system takes into account the interactions among drugs in order to minimize the adverse effects for the patient. We utilize the various attention weights in the system to compute the contributions from the information sources for the recommended medications. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Two case studies are also presented demonstrating that the justifications provided by PREMIER are appropriate and aligned to clinical practices.