RAHM: Relation augmented hierarchical multi-task learning framework for reasonable medication stocking

doi:10.1016/j.jbi.2020.103502

2020-08-01

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DOI：10.1016/j.jbi.2020.103502

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As an important task in digital preventive healthcare management, especially in the secondary prevention stage, active medication stocking refers to the process of preparing necessary medications in advance according to the predicted disease progression of patients. However, predicting preventive or even life-saving medicine for each patient is a non-trivial task. Existing models usually overlook the implicit hierarchical relation between patient's predicted diseases and medications, and mainly focus on single tasks (medication recommendation or disease prediction). To tackle this limitation, we propose a relation augmented hierarchical multi-task learning framework, named RAHM. which is capable of learning multi-level relation-aware patient representation for reasonable medication stocking. Specifically, the framework first leverages the underlying structural relations of Electronic Health Record (EHR) data to learn the low-level patient visit representation. Then, it uses a regular LSTM to encode the historical temporal disease information for disease-level patient representation learning. Further, a relation-aware LSTM (R-LSTM) is proposed to handle the relations between diseases and medication in longitudinal patient records, which can better integrate the historical information into the medication-level patient representation. In the learning process, two pseudo residual structures are introduced to mitigate the error propagation and preserve the valuable relation information of EHRs. To validate our method, extensive experiments have been conducted based on the real-world clinical dataset. The results demonstrate a consistent superiority of our framework over several baselines in suggesting reasonable stock medication.

Keyword:

Preventive healthcare management

Reasonable medication stocking

Hierarchical multi-task learning

Long short-term memory networks