Laboratory medicine · 2026
Routine laboratory panels classify internal medicine ICD-10 code groups: comparison with frontier large language models and laboratory-only specialist assessment.
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Yesil Yusuf, Medetalibeyoglu Alpay, Ademoglu Evin
Аннотация
INTRODUCTION: Routine laboratory panels are nearly universal, but the panels' joint information is underused. We evaluated contemporaneous classification of International Statistical Classification of Diseases, Tenth Revision (ICD-10) code groups from same-encounter laboratory results.
METHODS: We developed 17 eXtreme Gradient Boosting (XGBoost) classifiers in 242 648 adult internal medicine encounters using age, sex, and results from 34 assays, with ICD-10 codes used only as outcomes. Stratified 10-fold cross-validation assessed discrimination, calibration, and decision curve performance. Leakage-free recalibration and fold-specific thresholds targeted 95% specificity. Using an 80-patient temporal holdout cohort, we compared the model with 3 large language models accessed through an application programming interface and findings from 1 specialist physician.
RESULTS: The mean cross-validated area under the curve (AUC) was 0.893. With identical preprocessing, the mean AUC was 0.892 for XGBoost and 0.809 for logistic regression. Recalibration changed the mean intercept and slope to -0.002 and 0.999, respectively, and the Brier score from 0.099 to 0.044. The holdout micro-averaged AUC was 0.880 vs 0.729 to 0.739 for language models; paired differences were 0.141 to 0.151 (all P < .005). Matched-specificity sensitivity differences favored the model but were imprecise.
DISCUSSION: Routine panels contain substantial information about contemporaneous ICD-10 coding, supporting prospective multicenter evaluation rather than a diagnostic claim.
METHODS: We developed 17 eXtreme Gradient Boosting (XGBoost) classifiers in 242 648 adult internal medicine encounters using age, sex, and results from 34 assays, with ICD-10 codes used only as outcomes. Stratified 10-fold cross-validation assessed discrimination, calibration, and decision curve performance. Leakage-free recalibration and fold-specific thresholds targeted 95% specificity. Using an 80-patient temporal holdout cohort, we compared the model with 3 large language models accessed through an application programming interface and findings from 1 specialist physician.
RESULTS: The mean cross-validated area under the curve (AUC) was 0.893. With identical preprocessing, the mean AUC was 0.892 for XGBoost and 0.809 for logistic regression. Recalibration changed the mean intercept and slope to -0.002 and 0.999, respectively, and the Brier score from 0.099 to 0.044. The holdout micro-averaged AUC was 0.880 vs 0.729 to 0.739 for language models; paired differences were 0.141 to 0.151 (all P < .005). Matched-specificity sensitivity differences favored the model but were imprecise.
DISCUSSION: Routine panels contain substantial information about contemporaneous ICD-10 coding, supporting prospective multicenter evaluation rather than a diagnostic claim.