Wenxiu Xie
Biographic Data
| ID | 7895062 |
|---|---|
| NAME | Wenxiu Xie |
| GIVEN NAMES | Wenxiu |
| FAMILY NAME | Xie |
| SIGNATURE | XIE W |
| AFFILIATIONS | City University of Hong Kong |
| ORCID | 0000-0002-8528-5193 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
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…
Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers
We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient's languages in order to predict the likelihood of clinically significant mistakes or incomprehensible MT outputs based on the features of English source information as input to the MT systems. A MNB classifier was developed to provide intuitive probabilistic predictions of erroneous health transl…
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
Assessing Communicative Effectiveness of Public Health Information in Chinese
Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities and inequalities in developed and developing countries. Health information communication from the World Health Organization is governed by key principles including health information relevance, credibility, understandability, actionability, accessibility. Multilingual health information developed under these pri…
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…
Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers
We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient's languages in order to predict the likelihood of clinically significant mistakes or incomprehensible MT outputs based on the features of English source information as input to the MT systems. A MNB classifier was developed to provide intuitive probabilistic predictions of erroneous health transl…
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
Assessing Communicative Effectiveness of Public Health Information in Chinese
Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities and inequalities in developed and developing countries. Health information communication from the World Health Organization is governed by key principles including health information relevance, credibility, understandability, actionability, accessibility. Multilingual health information developed under these pri…
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 …
Artificial Intelligence (6 works) · Computer Science (6 works) · Biomedical Text Mining and Ontologies (4 works) · Health care (4 works) · Machine learning (4 works) · Classifier (UML (3 works) · Health Literacy and Information Accessibility (3 works) · Psychology (3 works) · Support vector machine (3 works) · Clinical decision support system (2 works)