mlsanityData to 14 Oct 2026

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

arXiv 2303.06980 ↗First posted 13 Mar 2023cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 68 references that are in the map. First posted on arXiv 13 Mar 2023.

Builds on

17 of 68 references.

References · 17

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  3. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  4. 2020Self-supervised Learning: Generative or Contrastivecited by 694
  5. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 250
  6. 2020CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patientscited by 171
  7. 2019MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIcited by 122
  8. 2021Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Seriescited by 96
  9. 2021Self-Supervised Pretraining Improves Self-Supervised Pretrainingcited by 82
  10. 2022Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Seriescited by 54
  11. 2017Detecting Cancer Metastases on Gigapixel Pathology Imagescited by 52
  12. 2020Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Reviewcited by 47
5 more
  1. 2021SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Datacited by 31
  2. 2021Self-supervised Autoregressive Domain Adaptation for Time Series Datacited by 29
  3. 2020Self-supervised transfer learning of physiological representations from free-living wearable datacited by 27
  4. 2020A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networkscited by 18
  5. 2023ECGAN: Self-supervised generative adversarial network for electrocardiographycited by 2

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Self-Supervised Dynamical System Representations for Physiological Time-Seriescited by 3
  2. 2025General Demographic Pre-trained Models for Enhancing Predictive Performance Across Diseases and Populationcited by 0
  3. 2025Interpretable Heart Disease Prediction via a Weighted Ensemble Model: A Large-Scale Study with SHAP and Surrogate Decision Treescited by 0