Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka

Reported Methods, Distributions, and Frequencies of Torture Globally: A Systematic Review and Meta-Analysis

Importance

Despite its prohibition by the United Nations Convention against Torture and other international treaties, torture has been perpetrated against countless individuals worldwide, and health care practitioners globally are increasingly encountering refugee torture survivors in their clinical practices. The methods, geographic distribution, and frequency of torture globally are not well described, which limits health care practitioners' ability to adequately diagnose and treat the sequelae of torture.

Objective

To rank the commonness of torture methods and identify the regions of the world with which they are associated.

Data Sources

For this systematic review and meta-analysis, Ovid MEDLINE, Ovid Embase, Web of Science, and The Cochrane Library were searched from inception to July 2021.

Study Selection

Included studies were peer-reviewed articles in English, contained an independent sample population of individuals who experienced torture, and outlined the type(s) of torture experienced. Excluded studies were not peer reviewed, lacked an independent sample population, or did not specify torture methods. Articles were chosen for inclusion by 2 independent and blinded reviewers, and a third, independent reviewer resolved discrepancies. Overall, 266 articles-15.3% of the 1739 studies initially identified for full review-met the inclusion criteria.

Data Extraction and Synthesis

Data abstraction and quality assessment followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data were extracted by 2 independent and blinded reviewers into predefined templates, and a third, independent reviewer resolved discrepancies. The risk of bias was evaluated using the Downs and Black Checklist.

Main Outcomes and Measures

Torture methods were ranked by their average frequencies, numbers of reporting studies, and numbers of countries wherein the methods occurred.

Results

A total of 9937 titles and abstracts were screened, and 266 studies encompassing 103 604 individuals (13 350 men, 5610 women, and 84 644 unspecified) were analyzed. Torture was reported for 105 countries; 21 methods accounted for 84% of all reported methods and 10 methods accounted for 78% of all physical tortures. The top 3 methods were beating or blunt-force trauma (reported in 208 studies and 59 countries; average frequency, 62.4%; 95% CI, 57.7%-67.1%), electrical torture (reported in 114 studies and 28 countries; average frequency, 17.2%; 95% CI, 15.0%-19.4%), and starvation or dehydration (reported in 65 studies in 26 countries; average frequency, 12.7%; 95% CI, 10.2%-15.2%). According to the Downs and Black appraisal tool, 50 studies were rated as good or excellent and 216 as fair or poor.

Conclusions and Relevance

The findings of this study suggest that torture remains widespread. Although innumerable torture methods exist, a limited number account for the vast majority of reported tortures. So that targeted therapies may be developed, additional investigation is needed to better elucidate the sequelae associated with the most common torture methods, described here.

Medication Regularity of Traditional Chinese Medicine in the Treatment of Aplastic Anemia Based on Data Mining

Objective

Aplastic anemia (AA) is an uncommon disease, characterized by pancytopenia and hypocellular bone marrow, but it is common in the blood system. The medication rules of traditional Chinese medicine (TCM) in the treatment of AA are not clear, for which it is worth exploring the medication rules by data mining methods.

Methods

This study used SPSS Modeler 18.0 and SPSS statistics to analyze the cases of AA from Zhejiang Provincial Hospital of Chinese Medicine (ZJHCM) from March 1, 2019, to March 1, 2022. Data mining methods, including frequency analysis, cluster analysis, and association rule learning, were performed in order to explore the medication rules for AA.

Results

(1) A total of 859 prescriptions, which met the inclusion criteria, consisted of 255 herbs. In descending order of the frequency of herbal medicine, we have Danggui, Huangqi, Shudihuang, Fuling, Gancao, Shanyao, Shanzhuyu, Baizhu, Dangshen, and Xianhecao. (2) Frequency analysis of herb properties: the Four Qi of 255 kinds of TCMs are mainly warm and neutral medicines. The Five Flavors are mainly sweet medicines, followed by bitter medicines. The main meridians are the liver, spleen, and kidney. (3) Clustering of medications: TCMs with the top 20 frequencies are classified into 9 groups by cluster analysis. (4) Association rule analysis of high-frequency herbs: using the Apriori algorithm, the results showed that there were 3 herb pairs with support of over 0.3 and 12 herb pairs with confidence above 0.85.

Conclusion

The basic pathogenesis of AA (Sui Lao) is spleen and kidney essence deficiency, Qi deficiency, and blood stasis. The main herbs have warm and neutral properties, sweet tastes, and liver, spleen, and kidney meridian tropisms, whose purpose is to tonify the kidney and invigorate the spleen, tonify Qi, and promote blood circulation.

Analysis on Medication Rules of Chinese Medicinal Herb Formulae in Uterine Subinvolution Treatment Based on Data Mining

Introduction

Uterine subinvolution, especially the subinvolution of the placental site, can be a life-threatening disease that induces secondary postpartum hemorrhage (PPH). Chinese Herbal Medicine has been widely used to improve postpartum recovery and treat uterine subinvolution for thousands of years. Yet, there are many potential laws hidden that are worth exploring.

Methods

Prescriptions treating uterine subinvolution were searched and collected to form datasets. Data mining methods including frequency analysis, cluster analysis, and association rule learning were performed to uncover the potent prescription laws of uterine subinvolution treatment.

Results

A total of 803 formulae involving 249 herbs were obtained. The top 6 most frequently used herbs were Angelicae Sinensis Radix (Danggui), Chuanxiong Rhizoma (Chuanxiong), Leonuri Herba (Yimucao), Persicae Semen (Taoren), Zingiberis Rhizoma Preparatum (Paojiang), and Radix Glycyrrhizae Preparata (Zhigancao). Most of the 249 herbs were being warm in properties, sweet in tastes, and mainly distributed to liver and spleen meridian tropisms. Deficiency-tonifying herbs accounted for the most proportion and heat-clearing herbs ranked the second, followed by blood-activating and stasis-eliminating herbs. 6 clusters were generated by hierarchical clustering, and 5 of them were of clinical significance. 78 rules with support values over 0.25, confidence values over 0.8, and lift values greater than 1 were generated by association rule learning.

Conclusion

The basic principles for uterine subinvolution treatment were deficiency-tonifying, heat-clearing, blood-activating, and stasis-eliminating. Herbs with warm properties, sweet tastes, and liver and spleen meridian tropisms are generally suitable. In addition, Sheng-Hua-Tang was the most frequently used formula for the treatment of uterine subinvolution, yet the dialectical prescriptions were diversified with different patterns/symptoms.

Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.