Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage

arXiv:2606.29750v1 Announce Type: new Abstract: 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

Source: arXiv cs.CL — read the full report at the original publisher.

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