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.

人工智能在医疗保健领域的应用具有革命性，特别是在基于变压器的大型语言模型 (LLM) 的最新进展方面。本文研究了将符号推理与语言建模相结合，以改进对非结构化临床文本的理解的能力。我们展示了这种组合方式提高了从非结构化记录中提取多个医学变量的效果。此外，我们还展示了无广告商业免费 LLM 的检索能力与商业 LLM 相媲美。最后，我们阐述了通过符号推理对 LLM 进行引导的必要性，因为仅仅使用 LLM 会导致性能最低。