Large Language Models for Biomedical Knowledge Graph Construction:
Information extraction from EMR notes
- URL: http://arxiv.org/abs/2301.12473v2
- Date: Sat, 9 Dec 2023 17:31:49 GMT
- Title: Large Language Models for Biomedical Knowledge Graph Construction:
Information extraction from EMR notes
- Authors: Vahan Arsenyan, Spartak Bughdaryan, Fadi Shaya, Kent Small, Davit
Shahnazaryan
- Abstract summary: We propose an end-to-end machine learning solution based on large language models (LLMs)
The entities used in the KG construction process are diseases, factors, treatments, as well as manifestations that coexist with the patient while experiencing the disease.
The application of the proposed methodology is demonstrated on age-related macular degeneration.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The automatic construction of knowledge graphs (KGs) is an important research
area in medicine, with far-reaching applications spanning drug discovery and
clinical trial design. These applications hinge on the accurate identification
of interactions among medical and biological entities. In this study, we
propose an end-to-end machine learning solution based on large language models
(LLMs) that utilize electronic medical record notes to construct KGs. The
entities used in the KG construction process are diseases, factors, treatments,
as well as manifestations that coexist with the patient while experiencing the
disease. Given the critical need for high-quality performance in medical
applications, we embark on a comprehensive assessment of 12 LLMs of various
architectures, evaluating their performance and safety attributes. To gauge the
quantitative efficacy of our approach by assessing both precision and recall,
we manually annotate a dataset provided by the Macula and Retina Institute. We
also assess the qualitative performance of LLMs, such as the ability to
generate structured outputs or the tendency to hallucinate. The results
illustrate that in contrast to encoder-only and encoder-decoder, decoder-only
LLMs require further investigation. Additionally, we provide guided prompt
design to utilize such LLMs. The application of the proposed methodology is
demonstrated on age-related macular degeneration.
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