{"doi":"10.1145/3746252.3760894","title":"Improving Rare and Common ICD Coding via a Multi-Agent LLM-Based Approach","abstract":"Large Language Models (LLMs) have shown strong performance in tasks such as zero- and few-shot information extraction from clinical text without domain-specific training. However, in the ICD coding task, LLMs often hallucinate key details and produce high-recall but low-precision outputs due to the high-dimensional and imbalanced nature of ICD code distributions. Existing LLM-based approaches typically fail to capture the complex, dynamic interactions among human agents involved in real-world coding workflows-such as patients, physicians, and coders-and often lack interpretability and reliability. To address these challenges, we propose a novel multi-agent framework for ICD coding that simulates the real-world process using five role-specific LLM agents-patient, physician, coder, reviewer, and adjuster-and integrates the Subjective, Objective, Assessment, and Plan (SOAP) structure from Electronic Health Records to enhance performance. Evaluated on the MIMIC-III dataset, our method significantly outperforms zero-shot Chain-of-Thought prompting, self-consistency strategies, and LLM-designed agent baselines, particularly for rare codes. Ablation studies confirm the contribution of each agent role, and the system achieves competitive performance with state-of-the-art fine-tuned models, while offering better explainability and requiring no task-specific pre-training.","journal":null,"year":2025,"id":580415,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9456,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1160100,"name":"Xun Wang","orcid":"0000-0002-1986-1172","position":1,"is_corresponding":false},{"id":496030,"name":"Hong Yu","orcid":"0000-0001-9263-5035","position":2,"is_corresponding":false},{"id":552464,"name":"Rumeng Li","orcid":"0000-0003-4584-7486","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T02:58:38.868285Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}