A Chrome App for Improving Reading Comprehension of Health Information Online for Individuals with Low Health Literacy

Thapa-Chhetry, Binod; Keck, Tia

doi:10.1109/seh.2019.00018

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Information technology was used as a data collection tool applied to HL [16][17][18][19], but it wasn't found that information technology was applied to assess HL level. However, with the rapid development of information science, big data analysis and machine learning technology had been applied widely in medical field to solve statistical problems.…”

Section: Introductionmentioning

confidence: 99%

Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China

Yan¹,

Zhang

2022

BMC Public Health

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Background It is difficult to accurately assess the health literacy(HL) level of Mongolians by using Chinese conventional HL questionnaire, due to their particularity in language, culture and living environment. Therefore, it is very important to design an exclusive HL questionnaire for them. In addition, the existing statistical models cannot meet the requirement of HL assessment with high precision, so it is necessary to study a new HL assessment model. Methods A HL questionnaire with 68 questions is designed by combing the HLS-EU-Q47and the characteristics of Mongolians in China. 742 Mongolians aged 18 to 87 in Inner Mongolia of China answered the questionnaire. A data set with 742 samples is constructed, where each sample has 68 features and 1 target. Based on it, the XGB and LGBM regression models are respectively constructed to assess the HL levels of respondents, and their evaluation effects are compared. The impact of each question on the HL level is quantitatively analyzed by using the feature-importance function in LGBM model to verify the effectiveness of the questionnaire and to find the key factors for affecting HL. Results The HL questionnaire has the high reliability, which is reflected by the high internal consistency (Cronbach’s coefficient=0.807) and test-retest reliability (Mutual Information Score= 0.803). The validity of the HL questionnaire is obtained by solving KMO and Bartlett Spherical Test Chi-square Value, which are 0.765 and 2486 ($$p<0.001$$ p < 0.001 ), respectively. $$R^2$$ R 2 index and the absolute error obtained by using the HL assessment model based on LGBM are 0.98347 and 11, which are better than ones by applying the model based-XGB, respectively. The quantitative analysis results show that all 68 questions have influence on HL level, but their degree are different. The first three factors are age, salary level, the judgment ability for the HL information in media, respectively. The HL level distribution of the respondents was 66.71$$\%$$ % excellent, 25.74$$\%$$ % good and 7.54$$\%$$ % poor, respectively. Conclusions The presented HL questionnaire with 68 questions and LGBM regression model can obtain the HL level assessment results with high precision for Mongolians in China. The impact of each question in the questionnaire on the final assessment results can be quantified by using the feature-importance function in LGBM model, which is better than the existing qualitative analysis methods.

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Section: Introductionmentioning

confidence: 99%

Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China

Yan¹,

Zhang

2022

BMC Public Health

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“…Machines help us in multiple ways: summarise our information, provide answers to our queries, and bring new ideas for us. There are growing appeals for the Machine Reading Comprehension (MRC) field, which is now being spun out into commercial applications [1][2][3] . However, there is a deficiency of such applications in low resource languages.…”

Section: Introductionmentioning

confidence: 99%

Machine Reading Comprehension: Methods and Trends of Low Resource Languages

2021

jecet

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Natural language processing (NLP) has been used to establish human-like communication with computers. Machine Reading Comprehension (MRC) has made a significant development; it has been crucial for machines to comprehend substantial language facets, like semantics, syntax, pragmatics, and phonology. Multiple studies have reported MRC models in high-resource languages. However, these models are unable to provide significant performance in MRC models in low resource languages. This is due to the unavailability of large-scale training datasets in low resource languages. Several studies on Machine Reading Comprehension (MRC) have proposed MRC models based on English. Nonetheless, these models provide insignificant performance on low-resource languages. However, limited research has been done using low resource languages, particularly Arabic, Urdu, and Hindi. This study presents a survey on trends and methods of Machine Reading Comprehension (MRC) in these low-resource languages. The survey demonstrates that available MRC models are ineffective in low resource languages, such as Hindi, Arabic, and Urdu, mainly due to large datasets and language structure unavailability. Finally, the study describes open issues in available MRC systems and provides direction for future research.

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Health Literacy Drift in Online Health Communities

Meng,

Zuo,

et al. 2023

2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)

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A Chrome App for Improving Reading Comprehension of Health Information Online for Individuals with Low Health Literacy

Cited by 4 publications

References 34 publications

Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China

Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China

Machine Reading Comprehension: Methods and Trends of Low Resource Languages

Health Literacy Drift in Online Health Communities

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