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
The proliferation of health data and the increasing reliance on AI for its analysis necessitate more sophisticated and accurate mapping techniques between disparate classification systems.
Improved automatic disease classification mapping enhances the integration of health data, critical for advanced medical research, public health initiatives, and personalized medicine.
The focus is shifting towards more complex one-to-many mappings in automatic disease classification, moving beyond simpler one-to-one methods to better reflect real-world medical data relationships.
- · Healthcare AI developers
- · Medical research institutions
- · Public health organizations
- · Health data integrators
- · Legacy manual classification systems
- · Organizations with siloed health data
More accurate and comprehensive disease classification mapping facilitates better data interoperability and analysis across different healthcare systems.
Enhanced health data analysis contributes to more precise epidemiological studies, improved diagnostic tools, and more effective treatment protocols.
The development of robust, AI-driven health data infrastructure could accelerate drug discovery, personalized medicine, and global health crisis response capabilities.
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