ARMR: Adaptively Responsive Network for Medication Recommendation
This work addresses medication recommendation for patients with complex conditions, offering an incremental improvement over existing methods.
The paper tackles the challenge of balancing historical medication reuse with new drug introduction in medication recommendation by proposing ARMR, which uses piecewise temporal learning and an adaptive mechanism to achieve better performance on MIMIC-III and MIMIC-IV datasets compared to state-of-the-art baselines.
Medication recommendation is a crucial task in healthcare, especially for patients with complex medical conditions. However, existing methods often struggle to effectively balance the reuse of historical medications with the introduction of new drugs in response to the changing patient conditions. In order to address this challenge, we propose an Adaptively Responsive network for Medication Recommendation (ARMR), a new method which incorporates 1) a piecewise temporal learning component that distinguishes between recent and distant patient history, enabling more nuanced temporal understanding, and 2) an adaptively responsive mechanism that dynamically adjusts attention to new and existing drugs based on the patient's current health state and medication history. Experiments on the MIMIC-III and MIMIC-IV datasets indicate that ARMR has better performance compared with the state-of-the-art baselines in different evaluation metrics, which contributes to more personalized and accurate medication recommendations. The source code is publicly avaiable at: https://github.com/seucoin/armr2.