Operationalizing Instability in Rule-Based Complete Blood Count Phenotyping Using Uncertainty-Aware Machine Learning

Background: Complete blood count (CBC) phenotypes are routinely assigned using deterministic rule-based thresholds. While operationally efficient, such rules may lead to unstable phenotype assignments for results close to clinical cutoffs in the presence of analytical variability. Methods: We analyzed routine CBC data from a tertiary care hospital laboratory. Rule-based phenotypes for anemia subtype, white blood cell (WBC) status, and platelet (PLT) status were assigned using established laboratory thresholds. A patient-independent development and holdout split was applied. A multi-output gradient boosting model was trained to reproduce rule-based labels and provide probabilistic outputs. Phenotype stability was assessed by perturbing CBC parameters under realistic analytical noise. Instability was defined as any change in phenotype assignment across perturbations. Distances to decision boundaries were grouped into quantile-based bins. Model uncertainty was evaluated for the triage of unstable cases. Results: Phenotype instability was strongly concentrated near decision boundaries. Under medium analytical variability, samples closest to hemoglobin cutoffs exhibited the highest instability, with the highest instability in the bin closest to the cutoff, a sharp decrease in the adjacent bin, and lower instability across more distant bins. Model uncertainty was enriched among unstable cases, enabling prioritization of borderline samples while reviewing only a subset of all cases. Conclusions: Rule-based CBC phenotyping exhibits intrinsic instability near decision thresholds. Uncertainty-aware machine learning supports a practical framework to identify and prioritize borderline cases without replacing existing laboratory rules, supporting workload-controlled post-analytical decision support.