Inpatient2Vec: Medical Representation Learning for Inpatients
This work addresses the need for specialized representation learning methods for inpatient data in healthcare, which is an incremental improvement over existing approaches.
The authors tackled the problem of representation learning for inpatient medical data, which has strong temporal relations and consistent diagnosis, by proposing Inpatient2Vec, a model that outperformed baselines on semantic similarity and clinical event prediction tasks using a real-world dataset.
Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that have different data characteristics. However, few of existing RL methods are specially designed for inpatients data, which have strong temporal relations and consistent diagnosis. In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning. In this work, weproposeInpatient2Vec, anovelmodel for learning three kinds of representations for inpatient, including medical activity, hospital day and diagnosis. A multi-layer self-attention mechanism with two training tasks is designed to capture the inpatient data characteristics and process the unordered set. Using a real-world dataset, we demonstrate that the proposed approach outperforms the competitive baselines on semantic similarity measurement and clinical events prediction tasks.