Modern epidemiological analytics increasingly use machine learning models that offer strong prediction but often lack calibrated uncertainty. Bayesian methods provide principled uncertainty quantification, yet are viewed as difficult to integrate with contemporary AI workflows. This paper proposes a unified Bayesian and AI framework that combines Bayesian prediction with Bayesian hyperparameter optimization. We use Bayesian logistic regression to obtain calibrated individual-level disease risk and credible intervals on the Pima Indians Diabetes dataset. In parallel, we use Gaussian-process Bayesian optimization to tune penalized Cox survival models on the GBSG2 breast cancer cohort. This yields a two-layer system: a Bayesian predictive layer that represents risk as a posterior distribution, and a Bayesian optimization layer that treats model selection as inference over a black-box objective. Simulation studies in low- and high-dimensional regimes show that the Bayesian layer provides reliable coverage and improved calibration, while Bayesian shrinkage improves AUC, Brier score, and log-loss. Bayesian optimization consistently pushes survival models toward near-oracle concordance. Overall, Bayesian reasoning enhances both what we infer and how we search, enabling calibrated risk and principled hyperparameter intelligence for epidemiological decision making.

翻译：现代流行病学分析日益采用机器学习模型，这些模型虽提供强大预测能力，但常缺乏校准的不确定性。贝叶斯方法提供了原则性的不确定性量化，但通常被认为难以与当代人工智能工作流集成。本文提出一个统一的贝叶斯与人工智能框架，将贝叶斯预测与贝叶斯超参数优化相结合。我们使用贝叶斯逻辑回归在Pima Indians糖尿病数据集上获得校准的个体水平疾病风险及可信区间。同时，我们采用高斯过程贝叶斯优化在GBSG2乳腺癌队列上调整惩罚Cox生存模型。这构建了一个双层系统：贝叶斯预测层将风险表示为后验分布，而贝叶斯优化层将模型选择视为对黑盒目标的推断。在低维与高维场景下的模拟研究表明，贝叶斯层提供了可靠的覆盖率和改进的校准性能，同时贝叶斯收缩提升了AUC、Brier分数和对数损失。贝叶斯优化持续推动生存模型接近最优一致性。总体而言，贝叶斯推理增强了我们推断的内容与搜索的方式，为流行病学决策实现了校准的风险和原则性的超参数智能。