Xibin Wang
Biographic Data
| ID | 9991809 |
|---|---|
| NAME | Xibin Wang |
| GIVEN NAMES | Xibin |
| FAMILY NAME | Wang |
| SIGNATURE | WANG X |
| AFFILIATIONS | Guizhou Institute of Technology |
| ORCID | 0000-0003-1831-9872 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Beyond Uniform Modeling
In real-world recommendation systems, user interactions contain behavioral information. Multibehavior sequential recommendation focuses on predicting the next item a user will interact with under a specific behavior. Incorporating behavioral information introduces diverse interaction patterns, which we treat as auxiliary signals to supplement trend interest modeling (e.g., primary interests, low-frequency interests, and noise). However, existing …
A Pattern Categorization of CT Findings to Predict Outcome of Covid-19 Pneumonia
Background As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival. This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia. Methods 165 patients with COVID-19 (91 men, 4-89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pa…
No prominent works on this page.
A Pattern Categorization of CT Findings to Predict Outcome of Covid-19 Pneumonia
Background As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival. This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia. Methods 165 patients with COVID-19 (91 men, 4-89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pa…
Beyond Uniform Modeling
In real-world recommendation systems, user interactions contain behavioral information. Multibehavior sequential recommendation focuses on predicting the next item a user will interact with under a specific behavior. Incorporating behavioral information introduces diverse interaction patterns, which we treat as auxiliary signals to supplement trend interest modeling (e.g., primary interests, low-frequency interests, and noise). However, existing …
Advanced Bandit Algorithms Research (1 work) · Behavioral modeling (1 work) · Comorbidity (1 work) · Confidence interval (1 work) · COVID-19 Clinical Research Studies (1 work) · COVID-19 diagnosis using AI (1 work) · Hazard ratio (1 work) · Intensive care medicine (1 work) · Internal Medicine (1 work) · Mechanical ventilation (1 work)