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Inf. Syst."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>Recent years have witnessed the rapid accumulation of massive electronic medical records, which highly support intelligent medical services such as drug recommendation. However, although there are multiple interaction types between drugs, e.g., synergism and antagonism, which can influence the effect of a drug package significantly, prior arts generally neglect the interaction between drugs or consider only a single type of interaction. Moreover, most existing studies generally formulate the problem of package recommendation as getting a personalized scoring function for users, despite the limits of discriminative models to achieve satisfactory performance in practical applications. To this end, in this article, we propose a novel end-to-end Drug Package Generation (DPG) framework, which develops a new generative model for drug package recommendation that considers the interaction effects between drugs that are affected by patient conditions. Specifically, we propose to formulate the drug package generation as a sequence generation process. Along this line, we first initialize the drug interaction graph based on medical records and domain knowledge. Then, we design a novel message-passing neural network to capture the drug interaction, as well as a drug package generator based on a recurrent neural network. In detail, a mask layer is utilized to capture the impact of patient condition, and the deep reinforcement learning technique is leveraged to reduce the dependence on the drug order. Finally, extensive experiments on a real-world dataset from a first-rate hospital demonstrate the effectiveness of our DPG framework compared with several competitive baseline methods.<\/jats:p>","DOI":"10.1145\/3511020","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:19:19Z","timestamp":1644869959000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":60,"title":["Interaction-aware Drug Package Recommendation via Policy Gradient"],"prefix":"10.1145","volume":"41","author":[{"given":"Zhi","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dazhong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Penggang","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoxing","family":"Huai","sequence":"additional","affiliation":[{"name":"Huawei Technologies"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian","family":"Wu","sequence":"additional","affiliation":[{"name":"Tencent"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"issue":"6","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1016\/j.jvs.2016.11.031","article-title":"Mining peripheral arterial disease cases from narrative clinical notes using natural language processing","volume":"65","author":"Afzal Naveed","year":"2017","unstructured":"Naveed Afzal, Sunghwan Sohn, Sara Abram, Christopher G. 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