Reply to “Machine Learning for Trauma Severity Scoring: External Validity, Bias, and Explainability”

Abstract

We thank Ardila and González-Arroyave [1] for their thoughtful commentary on our study [2], which highlights key considerations for translating machine learning (ML) to trauma registries. Their points on external validity, bias, explainability, portability, governance, and clinical value are well-received and align with our vision for scalable, trustworthy artificial intelligence (AI) in resource-limited settings. Regarding generalizability, we concur that multicenter external validation is essential. Our single-center design, while leveraging a large (n = 21,704) South African dataset, limits broader applicability due to variations in narrative styles, injury patterns, and languages. We reported strong performance across ISS thresholds (e.g., sensitivity 87.1% for ISS > 15), but calibration in diverse contexts is required. Future work will include external testing on international registries to assess agreement and portability [3]. On selection bias from training on complete cases, we acknowledge this risk. Completeness may correlate with case complexity or outcomes, as noted. Although we used stratified cross-validation to mitigate imbalance, we did not perform sensitivity analyses with injected missingness or semi-supervised methods. Preliminary checks showed no significant differences in demographics or injury mechanisms between complete and incomplete subsets (p > 0.05), but we agree that advanced techniques such as M-value analysis [4] could further test robustness and will be incorporated into extensions. For interpretability, random forest's ensemble nature provides inherent feature importance, revealing that injury descriptors (e.g., “penetrating wound”) and physiologic fields (e.g., GCS) drove predictions. However, SHAP explanations would enhance clinician trust by quantifying contributions and identifying errors at critical thresholds (e.g., false negatives near ISS > 15, which were minimal at 12.9% in our validation). Transparent error analyses, as suggested, could illuminate triage implications. Our high specificity (100%) supports safety, but we plan SHAP integration for future iterations. Portability relies on free-text NLP, avoiding ICD codes to prioritize unstructured narratives common in our setting. Comparing hybrid inputs (NLP + codes) is an excellent idea for interoperability; initial tests showed marginal gains (∼2% R2) with codes, but this warrants formal evaluation. Governance details—versioning via Git, monitoring for dataset shift, and annual recalibration—are underway to align with evolving practices [5]. Finally, downstream value was demonstrated via improved correlations with length of stay (r = 0.78 post-ML vs. 0.72 pre-ML; p < 0.001), suggesting better resource utilization. A prospective quasi-experimental study assessing real-time scoring's impact on time-to-decision is planned. In summary, these insights strengthen our approach. By addressing them, automated AIS/ISS can augment expert judgment, enhancing fair, reproducible trauma audits globally. G. L. Laing: writing – original draft, writing – review and editing, conceptualization, investigation, funding acquisition, methodology, validation, visualization, software, formal analysis, project administration, data curation, supervision, resources. The author has nothing to report. The author declares no conflicts of interest.

Ähnliche Arbeiten