Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals

Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection.

Correction: Lee et al. Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel. Int. J. Environ. Res. Public Health 2020, 17, 2618

The authors have noticed an inadvertent error in our article, ‘‘Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel” [...]

Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets

In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.

Enhancing the value of meat inspection records for broiler health and welfare surveillance: longitudinal detection of relational patterns

Background Abattoir data are under-used for surveillance. Nationwide surveillance could benefit from using data on meat inspection findings, but several limitations need to be overcome. At the producer level, interpretation of meat inspection findings is a notable opportunity for surveillance with relevance to animal health and welfare. In this study, we propose that discovery and monitoring of relational patterns between condemnation conditions co-present in broiler batches at meat inspection can provide valuable information for surveillance of farmed animal health and welfare. Results Great Britain (GB)-based integrator meat inspection records for 14,045 broiler batches slaughtered in nine, four monthly intervals were assessed for the presence of surveillance indicators relevant to broiler health and welfare. K-means and correlation-based hierarchical clustering, and association rules analyses were performed to identify relational patterns in the data. Incidence of condemnation showed seasonal and temporal variation, which was detected by association rules analysis. Syndrome-related and non-specific relational patterns were detected in some months of meat inspection records. A potentially syndromic cluster was identified in May 2016 consisting of infection-related conditions: pericarditis, perihepatitis, peritonitis, and abnormal colour. Non-specific trends were identified in some months as an unusual combination of condemnation reasons in broiler batches. Conclusions We conclude that the detection of relational patterns in meat inspection records could provide producer-level surveillance indicators with relevance to broiler chicken health and welfare.

Tree species consistent co-occurrence in seasonal tropical forests: an approach through association rules analysis

Aim of study: Aassessing the existence of consistent co-occurrence between tree species that characterize seasonal tropical forests, using the association rules analysis (ARA), that is a novel data mining methodology; and evaluate evaluating the taxonomic and functional similarities between associated species.Area of study: forty-four seasonal forest sites with permanent plots (40.2 ha of total sample) located in Southeast Brazil, from which we obtained species occurrences.Material and methods: we applied association rules analysis (ARA) to the dataset of species occurrence in sites considering the criteria of support equal to or greater than 0.63 and confidence equal to or greater than 0.8 to obtain the first set of associations rules between pairs of species. This set was then submitted to Fisher’s criteria exact p-value less than 0.05, lift equal to or greater than 1.1 and coverage equal to or greater than 0.63. We considered these criteria to be able to select non-random and consistent occurring associations.Main results: We obtained a final result of 238 rules for semideciduous forest and 11 rules for deciduous forests, composed of species characteristic of vegetation types. Co-occurrences are formed mainly by non-confamilial species, which have similar functional characteristics (potential size and wood density). There is a difference in the importance of co-occurrence between forest types, which tends to be less in deciduous forests.Research highlights: The results point to out the feasibility of applying ARA to ecological datasets as a tool for detecting ecological patterns of coexistence between species and the ecosystems functioning.Keywords: data mining; coexistence; semideciduous forests; deciduous forests; biotic interaction.

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

B40 community school children experience many underprivileged lifestyles which impacted their academic performance. Through data trends and patterns, the education top management can observe the progress of academic performance and the lifestyle relationships of students in a school or nearby school. It can also help to identify the causes of progress or deterioration of the performance of B40 community students. Therefore, a data analytics framework is essential to help decision-makers to see and analyze the changing trends and patterns of academic progress in data and related lifestyles of B40 community students more effectively and accurately. The objective of this study is to design and develop association rules analysis to deduce the relevance of academic achievement and lifestyle among schoolchildren from the B40 family. The analysis framework is established in several stages that involve data collection and processing and transformation, then the design, application and evaluate of the association rules algorithms. The framework is expected to benefit students, teachers and Education Ministry. This study foresees whether educational programs and healthy lifestyle awareness can be designed specifically for the B40 children so as to improve their academic achievement as desired by the government.