DeepAI AI Chat
Log In Sign Up

MedGCN: Graph Convolutional Networks for Multiple Medical Tasks

by   Chengsheng Mao, et al.

Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save cost on potentially redundant lab tests and inform physicians in more effective prescription. We present an intelligent model that can automatically recommend the patients' medications based on their incomplete lab tests, and can even accurately estimate the lab values that have not been taken. We model the complex relations between multiple types of medical entities with their inherent features in a heterogeneous graph. Then we learn a distributed representation for each entity in the graph based on graph convolutional networks to make the representations integrate information from multiple types of entities. Since the entity representations incorporate multiple types of medical information, they can be used for multiple medical tasks. In our experiments, we construct a graph to associate patients, encounters, lab tests and medications, and conduct the two tasks: medication recommendation and lab test imputation. The experimental results demonstrate that our model can outperform the state-of-the-art models in both tasks.


page 1

page 2

page 3

page 4


Graph Neural Pre-training for Enhancing Recommendations using Side Information

Leveraging the side information associated with entities (i.e. users and...

Representation Learning of EHR Data via Graph-Based Medical Entity Embedding

Automatic representation learning of key entities in electronic health r...

Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

The availability of a large amount of electronic health records (EHR) pr...

Processing of incomplete images by (graph) convolutional neural networks

We investigate the problem of training neural networks from incomplete i...

Temporal graph-based approach for behavioural entity classification

Graph-based analyses have gained a lot of relevance in the past years du...

PatientEG Dataset: Bringing Event Graph Model with Temporal Relations to Electronic Medical Records

Medical activities, such as diagnoses, medicine treatments, and laborato...

Decision Support for Intoxication Prediction Using Graph Convolutional Networks

Every day, poison control centers (PCC) are called for immediate classif...