Zhengkun Liu
Biographic Data
| ID | 7757024 |
|---|---|
| NAME | Zhengkun Liu |
| GIVEN NAMES | Zhengkun |
| FAMILY NAME | Liu |
| SIGNATURE | LIU Z |
| AFFILIATIONS | Tianjin University |
| ORCID | 0000-0002-6481-835X |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Machine learning for sudden cardiac death prediction among older adults using community-based electronic health records
ML models can significantly enhance the prediction of SCD risk using community-based EHRs. Our proposed risk model may enable the identification of high-risk individuals among older adults, facilitating targeted interventions and personalized care strategies. Future research should focus on integrating this model into routine primary care workflows and evaluating its effectiveness in real-world settings
Mathematical models and analysis tools for risk assessment of unnatural epidemics: A scoping review
Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven…
No prominent works on this page.
Mathematical models and analysis tools for risk assessment of unnatural epidemics: A scoping review
Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven…
Machine learning for sudden cardiac death prediction among older adults using community-based electronic health records
ML models can significantly enhance the prediction of SCD risk using community-based EHRs. Our proposed risk model may enable the identification of high-risk individuals among older adults, facilitating targeted interventions and personalized care strategies. Future research should focus on integrating this model into routine primary care workflows and evaluating its effectiveness in real-world settings
Biostatistics (1 works) · Computer Science (1 works) · Computer security (1 works) · COVID-19 epidemiological studies (1 works) · Data science (1 works) · Data-Driven Disease Surveillance (1 works) · ECG Monitoring and Analysis (1 works) · Electronic health record (1 works) · Engineering (1 works) · Health records (1 works)