机器学习（machine learning, ML）模型往往依赖于大规模的训练数据集，且在解释潜在变量方面存在不足。该文提出的创新性延迟潜在混合模型（delayed latent hybridization model, DLHM） 引入了分段常数延迟（piecewise-constant delays, PCDs）机制，以模拟药理学及疾病进展过程中不可避免的延迟现象。通过融入延迟机制，该研究在动态系统建模设计中加入了高层次的专家知识（即延迟），旨在提升模型在预测药理动态和疾病进展方面的性能，进而增强模型对患者的可解释性和沟通效率。研究结果表明，延迟潜在混合模型在疾病进展预测任务中显示出了优化的预测可靠性与一致性。该文利用COVID-19患者的合成数据对模型性能进行了验证，标志着在考虑延迟效应和专家知识的生物科学建模领域取得了显著进步。

Machine learning (ML) models often require large training datasets and lack the interpretability of latent variables. This novel delayed latent hybridization model (DLHM) incorporates piecewise-constant delays (PCDs) to model delays that are inevitably present in pharmacology and disease progression, a feature missing in existing approaches that leverage expert knowledge. By incorporating delays, we contributed a high-level expert knowledge in the design of dynamic systems modeling, which enhanced performance in predicting pharmacological and disease progression dynamics and aims to improve interpretability and communication to patients. Our findings indicate that DLHM demonstrates improved predictive reliability and congruence with the disease progression prediction task. The paper validates the model’s performance using synthetic data from COVID-19 patients, offering a significant advancement in biosciences modeling with delayed effects and expert knowledge.