  The application of Artificial Intelligence (AI) in healthcare has been
revolutionary, especially with the recent advancements in transformer-based
Large Language Models (LLMs). However, the task of understanding unstructured
electronic medical records remains a challenge given the nature of the records
(e.g., disorganization, inconsistency, and redundancy) and the inability of
LLMs to derive reasoning paradigms that allow for comprehensive understanding
of medical variables. In this work, we examine the power of coupling symbolic
reasoning with language modeling toward improved understanding of unstructured
clinical texts. We show that such a combination improves the extraction of
several medical variables from unstructured records. In addition, we show that
the state-of-the-art commercially-free LLMs enjoy retrieval capabilities
comparable to those provided by their commercial counterparts. Finally, we
elaborate on the need for LLM steering through the application of symbolic
reasoning as the exclusive use of LLMs results in the lowest performance.
