Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage
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 \emp...
| Authors | Santosh Purja Pun, Oliver Obst, Jim Basilakis et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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