The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise.

翻译：患者数据的固有特性带来了多重挑战。常见病例因患者数量庞大且随访规律而积累了大量纵向数据，然而纵向实验室数据以非规律性、时效性、缺失性和稀疏性著称；相比之下，罕见或特定病例的招募常受限于患者数量稀少及偶发性观察。本研究采用自监督学习预训练一个通用实验室进展模型，该模型能够捕捉常见心血管病例中六种常规实验室标志物的整体进展模式，旨在通过知识迁移辅助检测特定心血管事件。GLP模型实施两阶段训练策略，利用插值数据中嵌入的信息增强自监督学习性能。完成GLP预训练后，将其迁移至TVR检测任务。所提出的两阶段训练提升了纯自监督学习的性能，且GLP模型的迁移能力表现出独特性。经GLP处理后，分类性能显著提升，平均准确率从0.63提升至0.90。所有评估指标相较于未处理GLP时均表现出显著优越性（p<0.01）。本研究通过将一组患者的心血管实验室参数进展迁移至另一组患者，有效实现了转化工程，突破了数据可用性的局限。疾病进展的可迁移性优化了检查与治疗策略，在利用常规实验室参数的同时改善了患者预后。将该方法扩展至其他疾病的研究前景广阔。