MIMIC-III与MIMIC-IV上的自动化医学编码：一项批判性综述与可复现性研究
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
AI总结：
本文复现并比较自动化医学编码模型，修正macro F1计算与实验设置，分析稀有代码和文档长度影响，并发布MIMIC-III/IV公平评估流程。
摘要：
医学编码是为临床自由文本文档分配医学代码的任务。医疗专业人员手动分配这些代码，以追踪患者的诊断和治疗。自动化医学编码可显著减轻这一行政负担。本文复现、比较并分析了最先进的自动化医学编码机器学习模型。我们表明，若干模型表现不佳，原因在于配置薄弱、训练-测试划分抽样不当以及评估不充分。在以往工作中，macro F1分数的计算并非最优，而我们的修正使其翻倍。我们采用分层抽样和完全相同的实验设置（包括超参数和决策边界调优），贡献了经过修订的模型比较。我们分析预测误差，以验证和证伪以往工作的假设。分析证实，所有模型都难以处理稀有代码，而长文档仅产生可忽略的影响。最后，我们使用复现的模型，在最新发布的MIMIC-IV数据集上给出了首批全面结果。我们公开了代码、模型参数以及新的MIMIC-III和MIMIC-IV训练与评估流程，以支持未来的公平比较。
英文摘要：
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this administrative burden. In this paper, we reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. In previous work, the macro F1 score has been calculated sub-optimally, and our correction doubles it. We contribute a revised model comparison using stratified sampling and identical experimental setups, including hyperparameters and decision boundary tuning. We analyze prediction errors to validate and falsify assumptions of previous works. The analysis confirms that all models struggle with rare codes, while long documents only have a negligible impact. Finally, we present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models. We release our code, model parameters, and new MIMIC-III and MIMIC-IV training and evaluation pipelines to accommodate fair future comparisons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
