SeqMed: Recommending Medication Combination with Sequence Generative Adversarial Nets

Nowadays, the algorithmic advances in deep learning cause revolutionizing changes in the health-care domain. Many complex health-care problems get suitable solutions with deep learning-based methods, especially for Medicine Combination Prediction (MCP) to patients with complex health conditions. However, existing works either ignore the inter-relationships among medicines or fail to depict these relationships in an integrated and robust deep learning framework. To solve the problems above, in this paper, we propose SeqMed, a sequence generation model for predicting medicine combination. With the power of Generative Adversarial Nets (GAN), SeqMed can learn an expressive representation from medical records for the certain patient, and give accurate medicine recommendations for this patient. Experiments on real-world electronic medical record (EMR) dataset, MIMIC-III, show that SeqMed outperforms previous methods with a great leap. Meanwhile, SeqMed can stably converge. As a result, SeqMed achieves an improvement of 5.81% and 6.49% on the metrics of Jaccard and f1-score compared with the recent state-of-the-art method for MCP.