Liangjie Zhang
Biographic Data
| ID | 9993223 |
|---|---|
| NAME | Liangjie Zhang |
| GIVEN NAMES | Liangjie |
| FAMILY NAME | Zhang |
| SIGNATURE | ZHANG L |
| AFFILIATIONS | Shenzhen University |
| ORCID | 0000-0002-6219-0853 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Value of an automated machine learning model with post-hoc explanation for predicting healthcare-seeking delays among residents in Tibetan regions
Objective: This study aimed to investigate key determinants of healthcare-seeking delays among Tibetan residents and develop predictive models using automated machine learning (AutoML) with post-hoc SHAP interpretation alongside a clinical decision support system. Methods: = 376) subsets at an 8:2 ratio. Standardized preprocessing-including outlier rectification, one-hot encoding (OHE), and random forest-based multiple imputation (MI)-was applied…
A Review of Few-Shot and Zero-Shot Learning for Node Classification in Social Networks
Node classification tasks aim to assign labels or categories to entire graphs based on their structural properties or node attributes. It can be adopted for various types of graph systems, including but not limited to network traffic, biological networks, knowledge graphs, etc., especially to social networks. This problem is well-studied, and solutions have demonstrated significant success in numerous real-world applications. However, in the situ…
CGraphNet
Unlabeled text representation learning (UTRL), encompassing static word embeddings such as Word2Vec and contextualized word embeddings such as bidirectional encoder representations from transformer (BERT), aims to capture semantic word relationships in a low-dimensional space without the need for manual labeling. These word embeddings are invaluable for downstream tasks such as document classification and clustering. However, the surge of short t…
No prominent works on this page.
A Review of Few-Shot and Zero-Shot Learning for Node Classification in Social Networks
Node classification tasks aim to assign labels or categories to entire graphs based on their structural properties or node attributes. It can be adopted for various types of graph systems, including but not limited to network traffic, biological networks, knowledge graphs, etc., especially to social networks. This problem is well-studied, and solutions have demonstrated significant success in numerous real-world applications. However, in the situ…
CGraphNet
Unlabeled text representation learning (UTRL), encompassing static word embeddings such as Word2Vec and contextualized word embeddings such as bidirectional encoder representations from transformer (BERT), aims to capture semantic word relationships in a low-dimensional space without the need for manual labeling. These word embeddings are invaluable for downstream tasks such as document classification and clustering. However, the surge of short t…
Value of an automated machine learning model with post-hoc explanation for predicting healthcare-seeking delays among residents in Tibetan regions
Objective: This study aimed to investigate key determinants of healthcare-seeking delays among Tibetan residents and develop predictive models using automated machine learning (AutoML) with post-hoc SHAP interpretation alongside a clinical decision support system. Methods: = 376) subsets at an 8:2 ratio. Standardized preprocessing-including outlier rectification, one-hot encoding (OHE), and random forest-based multiple imputation (MI)-was applied…
Artificial Intelligence (2 works) · Computer Science (2 works) · Machine learning (2 works) · Advanced Graph Neural Networks (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Decision support system (1 work) · Domain Adaptation and Few-Shot Learning (1 work) · Engineering (1 work) · Feature learning (1 work) · Global Maternal and Child Health (1 work)