  Most of the existing medication recommendation models are predicted with only
structured data such as medical codes, with the remaining other large amount of
unstructured or semi-structured data underutilization. To increase the
utilization effectively, we proposed a method of enhancing medication
recommendation with Large Language Model (LLM) text representation. LLM
harnesses powerful language understanding and generation capabilities, enabling
the extraction of information from complex and lengthy unstructured data such
as clinical notes which contain complex terminology. This method can be applied
to several existing base models we selected and improve medication
recommendation performance with the combination representation of text and
medical codes experiments on two different datasets. LLM text representation
alone can even demonstrate a comparable ability to the medical code
representation alone. Overall, this is a general method that can be applied to
other models for improved recommendations.
