scholarly journals Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals

2021 ◽  
Vol 11 (22) ◽  
pp. 10828
Author(s):  
Jianxiang Wei ◽  
Jimin Dai ◽  
Yingya Zhao ◽  
Pu Han ◽  
Yunxia Zhu ◽  
...  
Keyword(s):  
Data Mining ◽  
Signal Detection ◽  
Association Rules ◽  
Public Health Problem ◽  
Detection Methods ◽  
Drug Risk ◽  
Spontaneous Reports ◽  
Optimal Thresholds ◽  
Data Source ◽  
Association Rules Analysis

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.

scholarly journals Association Rules Analysis on FP-Growth Method in Predicting Sales

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Mesran Mesran ◽  
Andre Hasudungan Lubis ◽  
Ali Ikhwan ◽  
Supiyandi
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Data Set ◽  
Pattern Processing ◽  
Large Databases ◽  
Growth Method ◽  
Association Rules Analysis ◽  
A Company

Sales transaction data on a company will continue to increase day by day. Large amounts of data can be problematic for a company if it is not managed properly. Data mining is a field of science that unifies techniques from machine learning, pattern processing, statistics, databases, and visualization to handle the problem of retrieving information from large databases. The relationship sought in data mining can be a relationship between two or more in one dimension. The algorithm included in association rules in data mining is the Frequent Pattern Growth (FP-Growth) algorithm is one of the alternatives that can be used to determine the most frequent itemset in a data set.

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

Computation ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 99
Author(s):  
Pannapa Changpetch ◽  
Apasiri Pitpeng ◽  
Sasiprapa Hiriote ◽  
Chumpol Yuangyai
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Classification Tree ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Association Rules Analysis

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.

Mining Generalized Association Rules in an Evolving Environment

2011 ◽  
pp. 1268-1274
Author(s):  
Wen-Yang Lin ◽  
Ming-Cheng Tseng
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
State Of The Art ◽  
Dynamic Environment ◽  
Static Data ◽  
Generalized Association Rules ◽  
Static Item ◽  
Data Source ◽  
Over Time ◽  
Art Techniques

The mining of Generalized Association Rules (GARs) from a large transactional database in the presence of item taxonomy has been recognized as an important model for data mining. Most previous studies on mining generalized association rules, however, were conducted on the assumption of a static environment, i.e., static data source and static item taxonomy, disregarding the fact that the taxonomy might be updated as new transactions are added into the database over time, and as such, the analysts may have to continuously change the support and confidence constraints, or to adjust the taxonomies from different viewpoints to discover more informative rules. In this chapter, we consider the problem of mining generalized association rules in such a dynamic environment. We survey different strategies incorporating state-of-the-art techniques for dealing with this problem and investigate how to efficiently update the discovered association rules when there are transaction updates to the database along with item taxonomy evolution and refinement of support constraint.

A Comprehensive Evaluation System of Association Rules Based on AHP

2013 ◽  
Vol 427-429 ◽  
pp. 1907-1910
Author(s):  
Jing Bo Yuan ◽  
Xiao Lin Wei ◽  
Shun Li Ding
Keyword(s):  
Data Mining ◽  
Analytic Hierarchy Process ◽  
Association Rules ◽  
Evaluation System ◽  
Comprehensive Evaluation ◽  
Analytic Hierarchy ◽  
Comprehensive Evaluation System ◽  
Association Rules Analysis ◽  
Mining Algorithms ◽  
Hierarchy Process

Association rules analysis is an important subject in data mining. At present, association rules mining algorithms frequently generate a large number of association rules, but most of the algorithm evaluations make advances only from an aspect, which makes the users select difficultly. Therefore, the comprehensive evaluation of association rules has become highly necessary. A comprehensive evaluation system of association rules based on the AHP (Analytic Hierarchy Process) was presented, which can evaluate the association rules from multi-angle and multi-dimensional. Many evaluation results are integrated into the system, eventually presenting a unified comprehensive coefficient to users. Practical data make it clear that the comprehensive evaluation system is rational and superior.

Optimal thresholds of early response to atypical antipsychotics: Application of signal detection methods

2009 ◽  
Vol 113 (1) ◽  
pp. 34-40 ◽  
Author(s):  
Lei Chen ◽  
Haya Ascher-Svanum ◽  
Virginia Stauffer ◽  
Bruce J. Kinon ◽  
Sara Kollack-Walker ◽  
...  
Keyword(s):  
Signal Detection ◽  
Atypical Antipsychotics ◽  
Early Response ◽  
Detection Methods ◽  
Optimal Thresholds

scholarly journals Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis (Preprint)

2018 ◽  
Author(s):  
Marie-Laure Kürzinger ◽  
Stéphane Schück ◽  
Nathalie Texier ◽  
Redhouane Abdellaoui ◽  
Carole Faviez ◽  
...  
Keyword(s):  
Signal Detection ◽  
Predictive Value ◽  
Empirical Bayes ◽  
Geometric Mean ◽  
Area Under The Curve ◽  
Detection Methods ◽  
Operating Characteristics ◽  
Web Based ◽  
Spontaneous Reports ◽  
Reporting Systems

BACKGROUND While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs). OBJECTIVE This study aimed (1) to assess the consistency of SDRs detected from patients’ medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems. METHODS Messages posted on patients’ forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided. RESULTS The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase. CONCLUSIONS The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients’ medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals.

scholarly journals Pharmacovigilance, signal detection using statistical data mining methods

2018 ◽  
Vol 7 (2.31) ◽  
pp. 122
Author(s):  
G V. Sriramakrishnan ◽  
M Muthu Selvam ◽  
K Mariappan ◽  
G Suseendran
Keyword(s):  
Data Mining ◽  
Signal Detection ◽  
Detection Methods ◽  
Risk Minimization ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Statistical Signal Detection ◽  
Statistical Data Mining ◽  
Mining Algorithms ◽  
Health Programmes

Pharmacovigilance programmes monitor and help safeguarding the use of medicines which is grave to the success of public health programmes. Identifying new possible risks and developing risk minimization action plans to prevent or ease these risks is at the heart of all pharmacovigilance activities throughout the product lifecycle.  In this paper we examine the use of data mining algorithms to identify signals from adverse events reported. The capabilities include screening, data mining and frequency tabulation for potential signals, including signal estimation using established statistical signal detection methods. We have standard processes, algorithms and follow current requirements for signal detection and risk management activities.

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

2021 ◽  
Vol 30 (2) ◽  
pp. e006
Author(s):  
Cléber Rodrigo Souza ◽  
Vinícius Andrade Maia ◽  
Natália Aguiar-Campos ◽  
Camila Laís Farrapo ◽  
Rubens Manoel Santos
Keyword(s):  
Data Mining ◽  
Tropical Forests ◽  
Association Rules ◽  
Tree Species ◽  
Total Sample ◽  
Deciduous Forests ◽  
Permanent Plots ◽  
P Value ◽  
Semideciduous Forest ◽  
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. 

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