  Large Language Models (LLMs) have demonstrated impressive and diverse
abilities that can benefit various domains, such as zero and few-shot
information extraction from clinical text without domain-specific training.
However, for the ICD coding task, they often hallucinate key details and
produce high recall but low precision results due to the high-dimensional and
skewed distribution of the ICD codes. Existing LLM-based methods fail to
account for the complex and dynamic interactions among the human agents
involved in coding, such as patients, physicians, and coders, and they lack
interpretability and reliability. In this paper, we present a novel multi-agent
method for ICD coding, which mimics the real-world coding process with five
agents: a patient agent, a physician agent, a coder agent, a reviewer agent,
and an adjuster agent. Each agent has a specific function and uses a LLM-based
model to perform it. We evaluate our method on the MIMIC-III dataset and show
that our proposed multi-agent coding framework substantially improves
performance on both common and rare codes compared to Zero-shot Chain of
Thought (CoT) prompting and self-consistency with CoT. The ablation study
confirms the proposed agent roles' efficacy. Our method also matches the
state-of-the-art ICD coding methods that require pre-training or fine-tuning,
in terms of coding accuracy, rare code accuracy, and explainability.
