Natural Language-Assisted Multi-modal Medication Recommendation
Journal:
arXiv
Published Date:
Jan 13, 2025
Abstract
Combinatorial medication recommendation(CMR) is a fundamental task of
healthcare, which offers opportunities for clinical physicians to provide more
precise prescriptions for patients with intricate health conditions,
particularly in the scenarios of long-term medical care. Previous research
efforts have sought to extract meaningful information from electronic health
records (EHRs) to facilitate combinatorial medication recommendations. Existing
learning-based approaches further consider the chemical structures of
medications, but ignore the textual medication descriptions in which the
functionalities are clearly described. Furthermore, the textual knowledge
derived from the EHRs of patients remains largely underutilized. To address
these issues, we introduce the Natural Language-Assisted Multi-modal Medication
Recommendation(NLA-MMR), a multi-modal alignment framework designed to learn
knowledge from the patient view and medication view jointly. Specifically,
NLA-MMR formulates CMR as an alignment problem from patient and medication
modalities. In this vein, we employ pretrained language models(PLMs) to extract
in-domain knowledge regarding patients and medications, serving as the
foundational representation for both modalities. In the medication modality, we
exploit both chemical structures and textual descriptions to create medication
representations. In the patient modality, we generate the patient
representations based on textual descriptions of diagnosis, procedure, and
symptom. Extensive experiments conducted on three publicly accessible datasets
demonstrate that NLA-MMR achieves new state-of-the-art performance, with a
notable average improvement of 4.72% in Jaccard score. Our source code is
publicly available on https://github.com/jtan1102/NLA-MMR_CIKM_2024.
