用分块匹配法提升疾病编码映射的精度与覆盖率。
Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage
- 先分块候选再用大模型匹配，解决复杂映射难题。
- 在多个版本间映射中实现高精度、高覆盖率。
- 适合医疗数据融合与长期分析的研究者使用。
自动映射疾病分类系统（如ICD）是整合健康数据与开展纵向分析的关键挑战。现有基于嵌入的方法多关注一对一映射，忽略更复杂的一对多场景。阈值法和top-K方法虽可扩展，但存在精确率、召回率与映射覆盖率之间的固有权衡——后者指至少有一个映射的目标代码的源代码比例。为此，我们提出一种新方法，受实体消歧中分块-匹配流程启发：首先生成候选匹配块（分块），再利用大语言模型（LLM）在每块内识别所有有效映射（匹配）。实验证明，该方法在多个ICD版本对（ICD-9-CM↔ICD-10-CM 和 ICD-10-AM↔ICD-11）上实现了更高精确率，同时保持相当召回率并扩大覆盖范围。源代码与数据集已公开：https://tinyurl.com/46kyn7wp。
原文摘要 · Abstract (English)
Automatic mapping between disease classification systems, such as the International Classification of Diseases (ICD), is a challenging yet essential task for integrating health data and conducting longitudinal data analysis. Existing embedding-based methods primarily focus on \emph{one-to-one} mappings, overlooking more complex \emph{one-to-many} scenarios. The threshold-based and top-K methods offer natural extensions; however, they involve inherent trade-offs between \emph{precision}, \emph{recall} and \emph{mapping coverage} -- the proportion of source codes with at least one mapping to a target code. To address this challenge, we introduce a novel method, which is inspired by the \emph{blocking-and-matching} pipeline commonly used in \emph{entity resolution}. In particular, we first generate a block of candidate matches (\emph{blocking}) and then employ a large language model (LLM) to identify all valid mappings within each block (\emph{matching}). Empirically, we show that the proposed method achieves higher precision with comparable recall and broader coverage across multiple ICD version pairs (ICD-9-CM$\leftrightarrow$ICD-10-CM and ICD-10-AM$\leftrightarrow$ICD-11). Our source code and dataset is available at: https://tinyurl.com/46kyn7wp.
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