Xiaobo Qian
Biographic Data
| ID | 7895064 |
|---|---|
| NAME | Xiaobo Qian |
| GIVEN NAMES | Xiaobo |
| FAMILY NAME | Qian |
| SIGNATURE | QIAN X |
| AFFILIATIONS | South China Normal University |
| ORCID | 0000-0001-5819-4379 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
The Impact of Climate Risks on Growth‐at‐Risk
This paper integrates climate risk into a growth‐at‐risk (GaR) framework to examine how climate transition and physical risks affect the distribution and downside tail of economic growth. Using city‐level panel data from 2008 to 2023, we find that both climate risks significantly raise downside growth risk across short, medium, and long horizons. The analysis further shows that these effects operate indirectly through total factor productivity an…
Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders
Background: Due to its convenience, wide availability, low usage cost, neural machine translation (NMT) has increasing applications in diverse clinical settings and web-based self-diagnosis of diseases. Given the developing nature of NMT tools, this can pose safety risks to multicultural communities with limited bilingual skills, low education, and low health literacy. Research is needed to scrutinise the reliability, credibility, usability of au…
Forecasting Erroneous Neural Machine Translation of Disease Symptoms
Our study demonstrated the viability, flexibility, and efficiency of introducing machine learning models to help promote risk-aware use of MT technologies to achieve optimal, safer digital health outcomes for vulnerable people
Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features
Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided autom…
Automatic Diagnosis of Mental Healthcare Information Actionability
We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental …
No prominent works on this page.
Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders
Background: Due to its convenience, wide availability, low usage cost, neural machine translation (NMT) has increasing applications in diverse clinical settings and web-based self-diagnosis of diseases. Given the developing nature of NMT tools, this can pose safety risks to multicultural communities with limited bilingual skills, low education, and low health literacy. Research is needed to scrutinise the reliability, credibility, usability of au…
Forecasting Erroneous Neural Machine Translation of Disease Symptoms
Our study demonstrated the viability, flexibility, and efficiency of introducing machine learning models to help promote risk-aware use of MT technologies to achieve optimal, safer digital health outcomes for vulnerable people
Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features
Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided autom…
Automatic Diagnosis of Mental Healthcare Information Actionability
We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental …
The Impact of Climate Risks on Growth‐at‐Risk
This paper integrates climate risk into a growth‐at‐risk (GaR) framework to examine how climate transition and physical risks affect the distribution and downside tail of economic growth. Using city‐level panel data from 2008 to 2023, we find that both climate risks significantly raise downside growth risk across short, medium, and long horizons. The analysis further shows that these effects operate indirectly through total factor productivity an…
Artificial Intelligence (4 works) · Computer Science (4 works) · Health care (3 works) · Machine learning (3 works) · Psychology (3 works) · Biomedical Text Mining and Ontologies (2 works) · Classifier (UML (2 works) · Health Literacy and Information Accessibility (2 works) · Machine translation (2 works) · Mental health (2 works)