Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population

Shi, Yulin; Hu, Xiaojuan; Cui, Ji; Cui, Longtao; Ma, Xuxiang; Jiang, Tao; Yao, Xinghua; Lan, Fang; Li, Jun; Bi, Zijuan; Li, Jiacai; Wang, Yu; Fu, Haoran; Wang, Jue; Lin, Yow−Jon; Bai, Jingxuan; Guo, Xiaojing; Tu, Liping; Xu, Jiatuo

doi:10.1186/s12911-021-01410-3

Cited by 9 publications

(8 citation statements)

References 35 publications

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“…In this study, the measurement model consisted of the formative measurement model (for obesity) and the reflective measurement model (for routine blood tests, lipid metabolism, liver function and characteristics of SV). We evaluated the inner model by estimating the path coefficients between latent variables, discriminant validity, significance values, etc., and evaluated the outer model by confirming the reliability and validity of the measurement indicators, ultimately, to verify the reasonableness of the assumed relationship in the model 22 .…”

Section: Resultsmentioning

confidence: 99%

Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach

Wang,

Lou,

et al. 2023

Sci Rep

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Subhealth is a transitional state between health and disease, and it can be detected through routine physical check-ups. However, the complexity and diversity of physical examination items and the difficulty of quantifying subhealth manifestations are the main problems that hinder its treatment. The aim of this study was to systematically investigate the physical examination performance of the subhealthy population and further explore the deeper relationships between indicators. Indicators were obtained for 878 subjects, including basic information, Western medicine indicators, inquiries of traditional Chinese medicine and sublingual vein (SV) characteristics. Statistical differences were analysed using R software. To explore the distribution of symptoms and symptom clusters in subhealth, partial least squares-structural equation modelling (PLS-SEM) was applied to the subhealth physical examination index, and a structural model was developed to verify whether the relationship chain between the latent variables was reasonable. Finally, the reliability and validity of the PLS-SE model were assessed. The most common subclinical clinical symptoms were limb soreness (37.6%), fatigue (31.6%), shoulder and neck pain (30.5%) and dry eyes (29.2%). The redness of the SV in the subhealthy group was paler than that in the healthy group (p < 0.001). This study validates the establishment of the directed acyclic relationship chain in the subhealthy group: the path from routine blood tests to lipid metabolism (t = 7.878, p < 0.001), the path from lipid metabolism to obesity (t = 8.410, p < 0.001), the path from obesity to SV characteristics (t = 2.237, p = 0.025), and the path from liver function to SV characteristics (t = 2.215, p = 0.027). The innovative application of PLS-SEM to the study of subhealth has revealed the existence of a chain of relationships between physical examination indicators, which will provide a basis for further exploration of subhealth mechanisms and causal inference. This study has identified the typical symptoms of subhealth, and their early management will help to advance the treatment of diseases.

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Section: Resultsmentioning

confidence: 99%

Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach

Wang,

Lou,

et al. 2023

Sci Rep

Self Cite

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“…Our research team has been continuously working on research related to tongue diagnosis technology and has established a relatively reliable analysis methodology for tongue and index, and has also published findings on tongue diagnosis (Zhang et al, 2017;Qiao et al, 2018;Li et al, 2021a,b;Shi et al, 2021). The index of tongue diagnosis mainly included the color and texture of tongue body and tongue coating and proportion of tongue coating.…”

Section: Discussionmentioning

confidence: 99%

“…Health Status Assessment Questionnaire Scale (H20 Scale) and the Information Record Form of Four Diagnosis of TCM (Copyright No. : 2016Z11L025702) (Shi et al, 2021) (as shown in Supplementary Material 1) were used to further define the state of fatigue.…”

Section: Inclusion and Exclusion Criteriamentioning

confidence: 99%

“…The tongue indices were from three color spaces, Lab, HIS, and YCrCb (Qi et al, 2016;Sun et al, 2016;Schiller et al, 2018), each tongue and pulse index had its medical meaning (Qi et al, 2016;Luo et al, 2018;Li et al, 2021b;Shi et al, 2021). The indices of tongue diagnosis and pulse diagnosis and their corresponding clinical meaning are shown in Supplementary Table 1.…”

Section: Collecting Clinical Tongue Data and Pulse Datamentioning

confidence: 99%

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A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

Shi

Yao

et al. 2022

Front. Physiol.

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BackgroundFatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis.MethodsTongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue.ResultsThe results showed that all the four classifiers over “Tongue & Pulse” joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively.ConclusionThis study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.

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“…Fatigue is one of the most common subhealth conditions (Shi et al, 2021). A previous study has found that the prevalence of fatigue among nurses was 61.5% (Fang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

The influence of workplace incivility on the fatigue of female nurses: The mediating effect of engagement

Zhang

Hui-na

Liu

et al. 2021

J Nursing Management

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Aims To identify the associations of workplace incivility, engagement and fatigue among Chinese female nurses, and further explore whether engagement can play a mediating role. Background Nurses are at a high risk of fatigue. However, no research has been done to examine the associations among nurses' workplace incivility, engagement and fatigue. Methods The cross‐sectional study was conducted in Jiangsu Province, China. Self‐administered questionnaires were distributed to 1200 female nurses, including The Fatigue Scale, Workplace Incivility Scale, Gallup Workplace Questionnaire and demographic variables. Valid responses were obtained from 1035 (86.3%) of participants. Hierarchical multiple regression analysis was performed to examine the associations among workplace incivility, engagement and fatigue. Results The mean fatigue score was 6.54 ± 3.07. Workplace incivility and engagement were related to fatigue (P < .01). Engagement partly mediated the association between workplace incivility and fatigue (a*b = 0.086, bias‐corrected 95% confidence interval [CI]: 0.059, 0.116; P < .01), and the proportion of the mediating effect accounted for by engagement was 33.0%. Conclusion Chinese female nurses suffered from high level of fatigue. The improvement of female nurses' engagement may be helpful to alleviate the impact of workplace incivility on fatigue. Implications for Nursing Management Managers should reduce workplace incivility of female nurses by promoting engagement to reduce fatigue.

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Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population

Cited by 9 publications

References 35 publications

Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach

Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

The influence of workplace incivility on the fatigue of female nurses: The mediating effect of engagement

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