← Back to all news
arXiv AI By Meysam Ghaffari, Bhaskar Sen, Nasim Sabetpour, Nina Fatehi, Animesh Agarwal, Carlos Morato

LearnActCoder: Role-Aware Error Memory for Adaptive Clinical Coding Agents

Read the original on arXiv AI →

LearnActCoder introduces a role‑aware error memory system for clinical coding agents, converting labeled errors into a structured Mistake Knowledge Database (MistakeKDB). The framework routes false‑negative lessons to a recall‑oriented coder and false‑positive lessons to a precision‑oriented judge, forming a Coder‑Judge pipeline. On MIMIC‑III and MIMIC‑IV datasets, the MistakeKDB improves CPT F1 by 5.9 percentage points and shifts ICD‑10 coding toward higher precision, while maintaining stable performance on held‑out notes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Aug 6

Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications

arXiv:2509. 08604v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety.

By Anran Li, Lingfei Qian, Mengmeng Du, Yu Yin, Yan Hu, Zihao Sun, Yihang Fu, Hyunjae Kim, Erica Stutz, Xuguang Ai, Qianqian Xie, Rui Zhu, Jimin Huang, Yifan Yang, Siru Liu, Yih-Chung Tham, Lucila Ohno-Machado, Hyunghoon Cho, Zhiyong Lu, Hua Xu, Qingyu Chen
llmsfine-tuningbenchmarkssafety
More like this →
arXiv Machine Learning
Oct 3

GLoC-EHR: Evidence-Cited Clinical Reasoning over Global Context and Local EHR Events

GLoC-EHR is a multimodal language model that processes electronic health records by combining a fixed-size global memory of the entire patient trajectory with a local memory of selected events. It generates hospital-course summaries and masked concept descriptions, then is fine‑tuned to cite evidence before answering clinical questions, using group relative policy optimization to reward correct, evidence‑supported responses. On MIMIC‑IV outcome tasks, GLoC‑EHR achieves the highest macro AUROC among compared models when answering directly, and maintains strong performance with evidence‑cited reasoning while adding distinct supported findings from the local memory.

By Chaiho Shin, Kwangsoo Kim
llmsfine-tuningmultimodal
More like this →
arXiv AI
Oct 1

Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training

The Agent Error Dataset (AED) presents 50,228 error–diagnosis pairs collected from 9,961 source tasks across 33 environments, 19 harness families, and 23 policy models in text‑based agent systems. A five‑stage Agentic Error‑to‑Training (AET) pipeline generates diagnoses and proposed corrections, verifies them against recorded evidence, and creates separate training views for diagnosis and actor recovery. Experiments show that first‑proposal corrections improve verifier pass rates from 18.4% to 51.1%, and fine‑tuning with full‑diagnosis data raises Qwen3‑8B’s exact‑step agreement from 47.2% to 63.6% on a holdout set.

By Kunlun Zhu, Xuyan Ye, Yibo Li, Cheng Qian, Beibin Li, Heng Ji
llmsagentsfine-tuning
More like this →
arXiv AI
Jul 22

Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval

arXiv:2607. 19020v1 Announce Type: cross Abstract: Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice.

By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud
rag
More like this →