Is the G-BERT model useful beyond the task considered?
It learns a representation of medical records. The learned representation (embeddings) can be used for other predictive tasks involving information from electronic health records.
Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal electronic health records (EHRs) from a small number of patients with multiple visits ignoring a large number of patients with a single visit (selection bias). Moreover, important hierarchical knowledge such as diagnosis hierarchy is not leveraged in the representation learning process. To address these challenges, we propose G-BERT, a new model to combine the power of Graph Neural Networks (GNNs) and BERT (Bidirectional Encoder Representations from Transformers) for medical code representation and medication recommendation. We use GNNs to represent the internal hierarchical structures of medical codes. Then we integrate the GNN representation into a transformer-based visit encoder and pre-train it on EHR data from patients only with a single visit. The pre-trained visit encoder and representation are then fine-tuned for downstream predictive tasks on longitudinal EHRs from patients with multiple visits. G-BERT is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.
The availability of massive electronic health records (EHR) data and the advances of deep learning technologies have provided unprecedented resource and opportunity for predictive healthcare, including the computational medication recommendation task. A number of deep learning models were proposed to assist doctors in making medication recommendation BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . They often learn representations for medical entities (e.g., patients, diagnosis, medications) from patient EHR data, and then use the learned representations to predict medications that are suited to the patient's health condition. To provide effective medication recommendation, it is important to learn accurate representation of medical codes. Despite that various considerations were handled in previous works for improving medical code representations BIBREF4 , BIBREF2 , BIBREF3 , there are two limitations with the existing work: To mitigate the aforementioned limitations, we propose G-BERT that combines the pre-training techniques and graph neural networks for better medical code representation and medication recommendation. G-BERT is enabled and demonstrated by the following technical contributions: