Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation

Biomedical data and benchmarks are highly valuable yet very limited in low-resource languages other than English such as Vietnamese. In this paper, we make use of a state-of-the-art translation model in English-Vietnamese to translate and produce both pretrained as well as supervised data in the biomedical domains. Thanks to such large-scale translation, we introduce ViPubmedT5, a pretrained Encoder-Decoder Transformer model trained on 20 million translated abstracts from the high-quality public PubMed corpus. ViPubMedT5 demonstrates state-of-the-art results on two different biomedical benchmarks in summarization and acronym disambiguation. Further, we release ViMedNLI - a new NLP task in Vietnamese translated from MedNLI using the recently public En-vi translation model and carefully refined by human experts, with evaluations of existing methods against ViPubmedT5

ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation

We present ViT5, a pretrained Transformer-based encoder-decoder model for the Vietnamese language. With T5-style self-supervised pretraining, ViT5 is trained on a large corpus of high-quality and diverse Vietnamese texts. We benchmark ViT5 on two downstream text generation tasks, Abstractive Text Summarization and Named Entity Recognition. Although Abstractive Text Summarization has been widely studied for the English language thanks to its rich and large source of data, there has been minimal research into the same task in Vietnamese, a much lower resource language. In this work, we perform exhaustive experiments on both Vietnamese Abstractive Summarization and Named Entity Recognition, validating the performance of ViT5 against many other pretrained Transformer-based encoder-decoder models. Our experiments show that ViT5 significantly outperforms existing models and achieves state-of-the-art results on Vietnamese Text Summarization. On the task of Named Entity Recognition, ViT5 is competitive against previous best results from pretrained encoder-based Transformer models. Further analysis shows the importance of context length during the self-supervised pretraining on downstream performance across different settings.Comment: NAACL SRW 2022. arXiv admin note: text overlap with arXiv:2110.0425

The associations of suspected covid-19 symptoms with anxiety and depression as modified by hemodialysis dietary knowledge: A multi-dialysis center study

[[abstract]]During the COVID-19 pandemic, it is essential to evaluate hemodialysis patients’ dietary knowledge, especially among those with COVID-19 related symptoms, in order to identify appropriate strategies in managing their mental health. The study’s purposes were to test the psychometric properties of the hemodialysis dietary knowledge (HDK) scale, and to investigate the modifying impact of HDK on the associations of suspected COVID-19 symptoms (S-COVID-19-S) with anxiety and depression among hemodialysis patients. A cross-sectional study was conducted from July 2020 to March 2021 at eight hospitals across Vietnam. Data of 875 hemodialysis patients were analyzed, including socio-demographic, anxiety (the generalized anxiety disorder scale, GAD-7), depression (the patient health questionnaire, PHQ-9), S-COVID-19-S, HDK, health literacy, and digital healthy diet literacy. Confirmatory factor analysis (CFA) and logistic regression models were used to analyze the data. The HDK scale demonstrates the satisfactory construct validity with good model fit (Goodness of Fit Index, GFI = 0.96; Adjusted Goodness of Fit Index, AGFI = 0.90; Standardized Root Mean Square Residual, SRMR = 0.05; Root Mean Square Error of Approximation, RMSEA = 0.09; Normed Fit Index, NFI = 0.96; Comparative Fit Index, CFI = 0.96, and Parsimony goodness of Fit Index, PGFI = 0.43), criterion validity (as correlated with HL (r = 0.22, p < 0.01) and DDL (r = 0.19, p < 0.01), and reliability (Cronbach alpha = 0.70)). In the multivariate analysis, S-COVID-19-S was associated with a higher likelihood of anxiety (odds ratio, OR, 20.76; 95% confidence interval, 95%CI, 8.85, 48.70; p < 0.001) and depression (OR, 12.95; 95%CI, 6.67, 25.14, p < 0.001). A higher HDK score was associated with a lower likelihood of anxiety (OR, 0.70; 95%CI, 0.64, 0.77; p < 0.001) and depression (OR, 0.72; 95%CI, 0.66, 0.79; p < 0.001). In the interaction analysis, the negative impacts of S-COVID-19-S on anxiety and depression were mitigated by higher HDK scores (p < 0.001). In conclusion, HDK is a valid and reliable tool to measure dietary knowledge in hemodialysis patients. Higher HDK scores potentially protect patients with S-COVID-19-S from anxiety and depression during the pandemic

Osteoporosis Risk in Hemodialysis Patients: The Roles of Gender, Comorbidities, Biochemical Parameters, Health and Diet Literacy

Osteoporosis is a common bone health disorder in hemodialysis patients that is linked with a higher morbidity and mortality rate. While previous studies have explored the associated factors of osteoporosis, there is a lack of studies investigating the impacts of health literacy (HL) and digital healthy diet literacy (DDL) on osteoporosis. Therefore, we aimed to investigate the associations of HL, DDL, and other factors with osteoporosis among hemodialysis patients. From July 2020 to March 2021, a cross-sectional study was conducted on 675 hemodialysis patients in eight hospitals in Vietnam. The data were collected by using the osteoporosis self-assessment tool for Asians (OSTA) and the 12-item short form of the health literacy questionnaire (HLS-SF12) on digital healthy diet literacy (DDL) and hemodialysis dietary knowledge (HDK). In addition, we also collected information about the socio-demographics, the clinical parameters, the biochemical parameters, and physical activity. Unadjusted and adjusted multinomial logistic regression models were utilized in order to investigate the associations. The proportion of patients at low, medium, and high levels of osteoporosis risk was 39.6%, 40.6%, and 19.8%, respectively. In the adjusted models, women had a higher likelihood of osteoporosis risk than men (odds ratio, OR, 3.46; 95% confidence interval, 95% CI, 1.86, 6.44; p p p = 0.003) and stomach ulcers (OR, 1.95; 95% CI, 1.01, 3.77; p = 0.048) were more likely to have a higher likelihood of osteoporosis risk than those without. The patients who had a higher waist circumference (WC), HL, and DDL were less likely to have a medium level of osteoporosis risk (OR, 0.95; 95% CI, 0.92, 0.98; p = 0.004; OR, 0.92; 95% CI, 0.88, 0.96; p p = 0.017, respectively) and a high level of osteoporosis risk (OR, 0.93; 95% CI, 0.89, 0.97; p = 0.001; OR, 0.89; 95% CI, 0.84, 0.94; p p = 0.008, respectively) compared with a low level of osteoporosis risk and to those with a lower WC, HL, and DDL. In addition, higher levels of hemoglobin (Hb) (OR, 0.79; 95% CI, 0.66, 0.95; p = 0.014), hematocrit (Hct) (OR, 0.95; 95% CI, 0.92, 0.99; p = 0.041), albumin (OR, 0.91; 95% CI, 0.83, 0.99; p = 0.030), and education (OR, 0.37; 95% CI, 0.16, 0.88; p = 0.025) were associated with a lower likelihood of a high level of osteoporosis risk. In conclusion, osteoporosis risk is highly prevalent in hemodialysis patients. Improved HL, DDL, education, WC, albumin, Hb, and Hct levels should be considered in preventing hemodialysis patients from developing osteoporosis