Bio-Inspired Artificial Intelligence Multimorbidity Score for Human–Machine Clinical Risk Stratification

Multimorbidity is strongly associated with mortality, but existing comorbidity indices often generalize poorly across populations because they depend on fixed disease lists and context-specific weights. We developed the large-language-model-based multimorbidity score (LLM-MMS), a prompt-based large-language-model-derived multimorbidity phenotype score, as a bio-inspired artificial intelligence score for human–machine clinical risk stratification and evaluated it for all-cause mortality prediction in 770,439 adults from 9 international longitudinal cohorts. Harmonized participant-level data were converted into standardized health narratives and processed without model fine-tuning. In the UK Biobank, LLM-MMS achieved strong discrimination, calibration, and prediction accuracy. Across 8 external cohorts, it maintained consistent performance, with C-indices of 0.67 to 0.86 and observed-to-expected ratios close to 1.00. This outperformed recalibrated conventional comorbidity indices in every cohort with absolute C-index gains of 0.12 to 0.21. Performance remained stable across sex and age subgroups, including adults younger than 60 years, and decision-curve analysis showed greater or comparable clinical net benefit in most cohorts. Proteomic and interpretability analyses supported the biological plausibility of LLM-MMS, linking high multimorbidity burden to inflammatory, metabolic, and tissue-damage pathways and identifying clinically coherent drivers of risk. These findings suggest that LLM-MMS offers a transportable and interpretable bio-inspired framework that integrates clinician-like multimodal reasoning with population-level validation for multimorbidity risk stratification across diverse populations.