LlamaCare: An Instruction Fine-Tuned Large Language Model for Clinical NLP

Abstract

Large language models (LLMs) have shown remarkable abilities in generating natural texts for various tasks across different domains. However, applying LLMs to clinical settings still poses significant challenges, as it requires specialized knowledge, vocabulary, as well as reliability. In this work, we propose a novel method of instruction fine-tuning for adapting LLMs to the clinical domain, which leverages the instruction-following capabilities of LLMs and the availability of diverse real-world data sources. We generate instructions, inputs, and outputs covering a wide spectrum of clinical services, from primary cares to nursing, radiology, physician, and social work, and use them to fine-tune LLMs. We evaluated the fine-tuned LLM, LlamaCare, on various clinical tasks, such as generating discharge summaries, predicting mortality and length of stay, and more. Using both automatic and human metrics, we demonstrated that LlamaCare surpasses other LLM baselines in predicting clinical outcomes and producing more accurate and coherent clinical texts. We also discuss the challenges and limitations of LLMs that need to be addressed before they can be widely adopted in clinical settings.

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