Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection
Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consiste...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| Online Access: | https://ieeexplore.ieee.org/document/10227304/ |
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| author | Li-Chin Chen Kuo-Hsuan Hung Yi-Ju Tseng Hsin-Yao Wang Tse-Min Lu Wei-Chieh Huang Yu Tsao |
| author_facet | Li-Chin Chen Kuo-Hsuan Hung Yi-Ju Tseng Hsin-Yao Wang Tse-Min Lu Wei-Chieh Huang Yu Tsao |
| author_sort | Li-Chin Chen |
| collection | DOAJ |
| description | Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, 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. Methods and procedures: 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 target vessel revascularization (TVR) detection. Results: 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 (<inline-formula> <tex-math notation="LaTeX">${p} < 0.01$ </tex-math></inline-formula>) compared to prior GLP processing. Conclusion: 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. Clinical impact: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment. |
| format | Article |
| id | doaj-art-6bfd0fa0e2dd449c8c3b7b22dedaba19 |
| institution | DOAJ |
| issn | 2168-2372 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-6bfd0fa0e2dd449c8c3b7b22dedaba192025-08-20T02:56:47ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722024-01-0112435510.1109/JTEHM.2023.330779410227304Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event DetectionLi-Chin Chen0https://orcid.org/0000-0002-2122-1625Kuo-Hsuan Hung1Yi-Ju Tseng2https://orcid.org/0000-0002-1814-5553Hsin-Yao Wang3Tse-Min Lu4Wei-Chieh Huang5https://orcid.org/0000-0003-3759-0131Yu Tsao6https://orcid.org/0000-0001-6956-0418Research Center for Information Technology Innovation, Academia Sinica, Taipei, TaiwanResearch Center for Information Technology Innovation, Academia Sinica, Taipei, TaiwanDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan City, TaiwanDepartment of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei, TaiwanDepartment of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei, TaiwanResearch Center for Information Technology Innovation, Academia Sinica, Taipei, TaiwanObjective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, 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. Methods and procedures: 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 target vessel revascularization (TVR) detection. Results: 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 (<inline-formula> <tex-math notation="LaTeX">${p} < 0.01$ </tex-math></inline-formula>) compared to prior GLP processing. Conclusion: 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. Clinical impact: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.https://ieeexplore.ieee.org/document/10227304/Cardiovascular diseasescardiometabolic diseasedisease progressionlaboratory examinationstime-series datapre-train model |
| spellingShingle | Li-Chin Chen Kuo-Hsuan Hung Yi-Ju Tseng Hsin-Yao Wang Tse-Min Lu Wei-Chieh Huang Yu Tsao Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection IEEE Journal of Translational Engineering in Health and Medicine Cardiovascular diseases cardiometabolic disease disease progression laboratory examinations time-series data pre-train model |
| title | Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection |
| title_full | Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection |
| title_fullStr | Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection |
| title_full_unstemmed | Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection |
| title_short | Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection |
| title_sort | self supervised learning based general laboratory progress pretrained model for cardiovascular event detection |
| topic | Cardiovascular diseases cardiometabolic disease disease progression laboratory examinations time-series data pre-train model |
| url | https://ieeexplore.ieee.org/document/10227304/ |
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