Translating machine learning predictions into meaningful risk estimates to support clinical decisions: a post hoc analysis of chronic obstructive pulmonary disease adverse outcomes using unified auto clinical scores

OBJECTIVE: The successful integration of Machine Learning (ML) models into clinical practice remains limited, as they often lack the standardized, quantifiable risk measures essential for clinical workflows. This study, therefore, aims to demonstrate the Unified Auto Clinical Scores (Uni-ACS) method as a means to translate ML predictions into interpretable biostatistical formats, a translation critical for clinical adoption and alignment with evidence-based practice guidelines. MATERIALS AND METHODS: We employed the Uni-ACS post hoc methodology to convert ML model outputs into clinical scores and odds ratios. Validation was performed on a retrospective cohort of Chronic Obstructive Pulmonary Disease patients admitted between 2016 and 2018, predicting adverse outcomes from 2019 to 2020. The performance of Uni-ACS was benchmarked against the original ML models and logistic regression. RESULTS: The successful application of Uni-ACS enabled the conversion of complex ML model outputs into readily interpretable clinical scores and metrics, like odds ratios. Clinical scores derived from the ML models' SHAP values using Uni-ACS maintained a strong, interpretable predictive performance (AUROC 0.69-0.80), which was comparable to the original ML models (AUROC 0.71-0.83) and logistic regression (AUROC 0.72-0.81). DISCUSSION: Uni-ACS addresses the fundamental clinical requirement for standardized, meaningful risk stratification. It efficiently translates complex ML predictions into validated clinical scores with minimal computational burden. CONCLUSION: In conclusion, this approach facilitates the widespread adoption of ML-driven risk assessment across diverse healthcare settings while ensuring compatibility with existing clinical guidelines and regulatory frameworks.