AI Med., Volume 1, Issue 2 (June 2026) – 3 articles

This study developed a two-stage, explainable machine learning framework to predict 18-month MMSE-based cognitive status from baseline multimodal data in community-dwelling older adults in Japan. A hierarchical design was used in which Stage 1 distinguished cognitively Normal participants from those with any abnormality (Possible Mild Cognitive Impairment (MCI) or Impaired), and Stage 2 further separated Possible MCI from Impaired within the abnormal subgroup. Both an Imbalanced-Learn Random Forest and a penalized logistic regression baseline were trained under Leave-One-Out Cross-Validation, yielding fair discrimination in Stage 1 (Random Forest AUC = 0.72, accuracy = 0.71; logistic regression AUC = 0.71, accuracy = 0.76) and apparently strong separability in Stage 2 (Random Forest AUC = 0.95, accuracy = 0.96; logistic regression AUC = 0.82, accuracy = 0.92) in a small sample size with high class imbalance. SHapley Additive exPlanations (SHAP) with TreeExplainer for Random Forest and LinearExplainer for logistic regression were used to identify interpretable biomarkers at each stage though feature attribution. In Stage 1, both models highlighted renal and systemic metabolic markers (e.g., creatinine, uric acid, blood urea nitrogen), amino acid and redox-related metabolites (including D-serine, D-amino acid proportions, L-asparagine, alanine, L-glutamic acid, cysteine, methionine sulfoxide), and wearable-derived activity variability (e.g., fluctuation coefficients and steps per minute), with the Simpson index of gut microbiome diversity also contributing in the logistic model. In Stage 2, the models converged on a distinct signature involving glucose and albumin, uric acid and uridine, choline and carnitine, multiple amino acids (such as phenylalanine, proline, ornithine, tryptophan, threonine, and short-chain amino acids), oxidative/energy markers (niacinamide, methionine, methionine sulfoxide, ergothioneine), hematologic indices, and high-MET activity fluctuation metrics. Collectively, these results support a stage-dependent, multisystem view of cognitive aging in which broad renal–metabolic, amino acid, and behavioral vulnerabilities characterize early abnormality, whereas more pronounced alterations in energy metabolism, nucleotide and choline pathways, oxidative stress, and activity irregularity accompany progression from Possible MCI to Impaired status. By combining routine clinical chemistry, targeted metabolomics, gut microbiome diversity, and wearable-derived behavioral measures within an explainable AI framework, this two-stage approach illustrates a scalable, biologically grounded strategy for stage-aware risk stratification and monitoring of cognitive decline in community settings. Full article

Glucocorticoid-induced hypertension affects over 30% of treated patients, yet its underlying mechanisms remain unclear, particularly how glucocorticoids regulate renin within the renin-angiotensin-aldosterone system (RAAS). Modeling these dynamics is difficult because only four dose-response measurements are available at a single 24-h timepoint (36 observations total), while the system depends on roughly eleven biochemical parameters spanning minutes-long receptor interactions to days-long protein secretion. Classical parameter estimation becomes unreliable in this extremely underdetermined setting, and purely data-driven methods offer limited biological interpretability. In this paper, we introduce a physics-informed neural network (PINN) framework that integrates ELISA measurements from As4.1 juxtaglomerular cells, ordinary differential equations describing glucocorticoid receptor signaling and renin transcription, and automatic differentiation to enforce mechanistic constraints. By systematically tuning synthetic-data weights (SW in {0.2, 0.3, 0.5}), we identify an intermediate value of SW = 0.3 that provides the best overall balance between predictive accuracy, accepted ensemble size, and biologically plausible parameter estimates among the tested configurations. The framework uses adaptive constraint scheduling with a plateau ramp to reduce premature convergence and introduces calibrated plausibility thresholds reflecting experimental noise. The accepted PINN ensemble (n = 5, 50% success rate) achieved R² = 0.803, compared with 0.759 for the SW = 0.5 baseline and −0.220 for the ODE-only baseline, with RMSE = 0.024. Key learned parameters (IC₅₀ = 2.925 ± 0.012 mg/dL, Hill = 1.950 ± 0.009) are biologically plausible within the model assumptions, and the best single accepted model attained R² = 0.891. Information criteria favored the PINN over the ODE model, with improvements of approximately 77× (AIC) and 5.9× (BIC). Despite training on a single timepoint, the PINN also infers full 48-h trajectories and reproduces non-monotonic dose-response behavior. This work presents, to our knowledge, the first PINN framework for glucocorticoid-mediated renin regulation and should be interpreted as a proof-of-concept approach for integrating sparse biomedical data with mechanistic constraints. The inferred parameters and temporal dynamics are best viewed as model-dependent, hypothesis-generating estimates rather than validated biological quantities. Full article

The exponential growth of unstructured medical data, particularly clinical notes and diagnostic reports, presents mounting challenges for healthcare knowledge extraction and utilization. This study introduces the Digital Medical Catalog (DMC), a framework that automates the conversion of clinical narratives into an auditable, semantically structured knowledge base. The framework combines BioClinicalBERT embeddings, c-TF-IDF statistical grounding, and semantic clustering, enabling high-fidelity classification (Macro F1 = 0.877 ± 0.012), traceable topic labeling, and temporal trend analysis. By demonstrating that semantic representation methods, reinforced with statistical grounding, are essential for large-scale medical text processing, this work establishes a foundation for privacy-preserving data governance and real-time intelligence within modern healthcare infrastructures. Full article