CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state
<|reference_start|>CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state: Medication recommendation systems are developed to recommend suitable medications tailored to specific patient. Previous researches primarily focus on learning medication representations, which have yielded notable advances. However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating. To address these limitations, we propose CausalMed, a patient health state-centric model capable of enhancing the personalization of patient representations. Specifically, CausalMed first captures the causal relationship between diseases/procedures and medications through causal discovery and evaluates their causal effects. Building upon this, CausalMed focuses on analyzing the health state of patients, capturing the dynamic differences of diseases/procedures in different health states of patients, and transforming diseases/procedures into medications on direct causal relationships. Ultimately, CausalMed integrates information from longitudinal visits to recommend medication combinations. Extensive experiments on real-world datasets show that our method learns more personalized patient representation and outperforms state-of-the-art models in accuracy and safety.<|reference_end|>
@article{li2024causalmed:,
    title={CausalMed: Causality-Based Personalized Medication Recommendation
  Centered on Patient health state},
    author={Xiang Li, Shunpan Liang, Yu Lei, Chen Li, Yulei Hou, Tengfei Ma},
    journal={arXiv preprint arXiv:2404.12228},
    year={2024},
    doi={10.1145/3627673.3679542},
    archivePrefix={arXiv},
    eprint={2404.12228},
    primaryClass={cs.AI cs.LG}
}