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The inherent nature of patient data poses several challenges. Prevalent casesamass substantial longitudinal data owing to their patient volume andconsistent follow-ups, however, longitudinal laboratory data are renowned fortheir irregularity, temporality, absenteeism, and sparsity; In contrast,recruitment for rare or specific cases is often constrained due to theirlimited patient size and episodic observations. This study employedself-supervised learning (SSL) to pretrain a generalized laboratory progress(GLP) model that captures the overall progression of six common laboratorymarkers in prevalent cardiovascular cases, with the intention of transferringthis knowledge to aid in the detection of specific cardiovascular event. GLPimplemented a two-stage training approach, leveraging the information embeddedwithin interpolated data and amplify the performance of SSL. After GLPpretraining, it is transferred for TVR detection. The proposed two-stagetraining improved the performance of pure SSL, and the transferability of GLPexhibited distinctiveness. After GLP processing, the classification exhibited anotable enhancement, with averaged accuracy rising from 0.63 to 0.90. Allevaluated metrics demonstrated substantial superiority (p < 0.01) compared toprior GLP processing. Our study effectively engages in translationalengineering by transferring patient progression of cardiovascular laboratoryparameters from one patient group to another, transcending the limitations ofdata availability. The transferability of disease progression optimized thestrategies of examinations and treatments, and improves patient prognosis whileusing commonly available laboratory parameters. The potential for expandingthis approach to encompass other diseases holds great promise.

Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection