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Abstract
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The following research has paramount importance in a significant number of aspects of healthcare informatics. On a scientific side, the research described here is going to make a full comparative assessment of encoder-only, decoder-only, and encoder-decoder LLM architecture configurations aimed specifically at a KG construction in the healthcare domain from clinical texts, ensuring proper foundations for further research within this area. Creation of a set of proven prompt engineering solutions for decoder-only models is going to ensure the usability potential of mighty generative language models within the scope of the problem of structured information extraction. On a clinical side, a full automated construction of a KG is going to make a significant number of breakthrough healthcare applications, such as a set of highly impactful tasks within the realm of drug reuse, trial design, confounding variable determination, and determination of hidden associations within a healthcare domain [3, 5]. The high potential efficiency of a full, proper extraction of clinical knowledge from a set of EMR clinical texts is going to make a significant step within evidence-based healthcare decisions, patient success improvement, and accelerated discoveries within a healthcare domain. The application within a real-world environment of healthcare with expert annotations is going to make research findings directly usable within a real-world healthcare environment, skipping an ordinal plan within a scientific research setting.
Moreover, the research tackles significant safety issues by exploring LLM’s tendency to hallucinate, as well as other qualitative properties that are vital in a healthcare domain, particularly when it comes to the development of a life-critical application, because even tiny inaccuracies might result in serious repercussions [16]. By applying the approach to “a globally prevalent health condition, the research work is going to bring forth concrete results that can
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