Temporal Self-Attention Network for Medical Concept Embedding

Understand

In longitudinal electronic health records (EHRs), the event records of a patient are distributed over a long period of time and the temporal relations between the events reflect sufficient domain knowledge to benefit prediction tasks such as the rate of inpatient mortality.

• Medical concept embedding as a feature extraction method that transforms a set of medical concepts with a specific time stamp into a vector, which will be fed into a supervised learning algorithm.
• The quality of the embedding significantly determines the learning performance over the medical data.
• In this paper, we propose a medical concept embedding method based on applying a self-attention mechanism to represent each medical concept.