GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Shang, Junyuan; Xiao, Cao; Ma, Tengfei; Li, Hongyan; Sun, Jimeng

doi:10.1609/aaai.v33i01.33011126

Cited by 177 publications

(126 citation statements)

References 2 publications

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“…More recently, researchers explored neural network-based models over clinical databases. Shang et al [17] combined graph structure with the memory network to recommend a personalized medication. The longitudinal electronic health records and drug-drug interaction information were embedded as a separate graph to be jointly considered for the recommendation.…”

Section: Related Workmentioning

confidence: 99%

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Kwak

Lee

Yoon

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than premarketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develop a novel graphbased framework for ADR signal detection using healthcare claims data. We construct a Drug-disease graph with nodes representing the medical codes. The edges are given as the relationships between two codes, computed using the data. We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database. The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships. Furthermore, our model predicts ADR pairs that do not exist in the established ADR database, showing its capability to supplement the ADR database.

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Section: Related Workmentioning

confidence: 99%

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Kwak

Lee

Yoon

et al. 2020

Advances in Knowledge Discovery and Data Mining

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“…There are other interesting approaches very recently reported in the knowledge graph based recommendation literatures such as unifying task oriented knowledge graph learning and recommendation [14], differentiated fashion recommendation using knowledge graph and data augmentation [15], a semantics driven knowledge graph for food recommendation [16], an attentionenhanced, knowledge-aware user preference model for recommendation [17], a graph augmented memory network for recommending medication combination [18], location embeddings for next trip recommendation [19], etc. which are worth mentioning in this paper.…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

IJMLNCE Editorial Note Volume No 03, Issue No 03

Solanki¹,

Díaz²

2019

IJMLNCE

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peer-reviewed, international journal, focuses on publishing authentic and unpublished quality research papers in the emerging field of Machine Learning and Collaborative Engineering. This is a scientific journal aims to promote highly qualitative and innovative research works, not limited to but focuses on the area of machine learning, collaborative engineering, and allied areas. We publish only original research articles, review articles, and technical notes.

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Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

Sajisha

Anoop

Ansal³

2019

IJMLNCE

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he unprecedented growth of unstructured data poses many challenges in semantic computing, which is an active research area for many years. While unearthing interesting patterns such as entities, relationships, and other metadata are important, it is equally important to represent them in an efficient, easy to access manner. Knowledge Graphs (KGs) are one such mechanism to represent facts extracted from unstructured text. KGs represent entities as nodes and relationships as edges. Such a representation may find applications in many meaning-aware computing applications such as question answering, summarization, etc., to name a few. Very recently, knowledge graph-based recommendation systems have become popular which has many advantages over traditional recommendation engines. This survey is an attempt to summarize and critically evaluate some of the very recent approaches to knowledge graph-based recommendation approaches.

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GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Cited by 177 publications

References 2 publications

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

IJMLNCE Editorial Note Volume No 03, Issue No 03

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

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