Diagnosis coding is a large-scale multi-label task in which each clinical encounter may require one or more coding labels from a large label space. Conventional top-k and threshold-based classifiers provide practical coding suggestions but do not directly characterize uncertainty over alternative coding sets. This study proposes sparse Mahalanobis conformal scoring for set prediction in diagnosis coding under extreme multi-label classification (XMC), intended for coding-assist workflows that require compact and reviewable coding suggestions. A sparse XMC model first generates candidate coding labels for each encounter. Candidate label sets are then constructed from the sparse proposal space and scored using a diagonal Mahalanobis nonconformity function calibrated on held-out data. Empirical conformal p-values are assigned to candidate sets, and downstream decision rules are used to obtain a final coding output from the retained region. The framework was evaluated using outpatient EHR data from a tertiary-care hospital, comprising approximately 8.0 million visits from 2018 to 2025 and up to 12,829 diagnosis labels. The primary SMaCS output achieved Micro-F1 close to the strongest threshold-based comparator and the highest exact match ratio among flexible-size decision rules. Compared with the other nonconformity scores, the Mahalanobis score produced a smaller retained region with fewer distinct labels, while preserving the same point-prediction performance. Additional analyses examined conformal region validity, robustness to label-frequency thresholds, code-depth performance, label-frequency subgroups, sample cardinality, department-level variation, and confidence–credibility stratification. Our results suggest that sparse Mahalanobis conformal scoring provides a useful framework for uncertainty-informed outpatient coding set prediction, while also highlighting the importance of candidate-space adequacy in extreme multi-label diagnosis coding.
Tangudomkit et al. (Fri,) studied this question.