Exploration of the Association Rules Mining Technique for the Signal Detection of Adverse Drug Events in Spontaneous Reporting Systems

SummaryBackground: Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations.Objective: We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time.Methods: We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm.Results: The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold.Discussion: The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches.Conclusion: The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.Citation: Scott J, Botsis T, Ball R. Simulating adverse event spontaneous reporting systems as preferential attachment networks: Application to the Vaccine Adverse Event Reporting System. Appl Clin Inf 2014; 5: 206–218 http://dx.doi.org/10.4338/ACI-2013-11-RA-0097

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Data Mining technique and its Application: Association rules mining for Korean language

10.14257/astl.2015.99.41 ◽

2015 ◽

Author(s):

Irfan Ajmal Khan ◽

Junghyun Woo ◽

Jin-Tak Choi

Keyword(s):

Data Mining ◽

Association Rules ◽

Korean Language ◽

Data Mining Technique ◽

Association Rules Mining ◽

Mining Technique

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Career and Skills Recommendations using Data Mining Technique: Matching Right People for Right Profession, in Pakistani Context

VFAST Transactions on Software Engineering ◽

10.21015/vtse.v13i3.510 ◽

2018 ◽

pp. 93-101

Author(s):

◽

Keyword(s):

Data Mining ◽

Association Rules ◽

Educational Background ◽

The Internet ◽

Job Seekers ◽

Data Mining Technique ◽

Association Rules Mining ◽

Mining Technique ◽

Using Data ◽

Different Sources

There are a number of recommendation systems available on the internet for the help of jobseekers. These systems only generate job recommendations for people on the basis of input entered by user. The problem observed in Pakistani people is they are not clear in which field they should start or switch working. Before searching and applying for a job, one should be clear about his/her profession and important skills regarding selected profession. Based on above issues, there is a need to design such a system that can overcome the problem of profession selection and skills suggestions so that it can be easy for a jobseeker to apply for a specific job. In this research, the problem which is discussed above is resolved by proposing a model by using Association Rules Mining, a data mining technique. In this model, professions are recommended to job seekers by matching the profile of applicant or job seeker with those persons who have same profile like educational background, professional skills and the type of jobs which they are doing. The data collected for this research itself is a major contribution as we collected it from different sources. We will make this data publically available for others so that they can use for further research.

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Improved privacy preserving method for periodical SRS publishing

PLoS ONE ◽

10.1371/journal.pone.0250457 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0250457

Author(s):

Wei Huang ◽

Tong Yi ◽

Haibin Zhu ◽

Wenqian Shang ◽

Weiguo Lin

Keyword(s):

Theoretical Analysis ◽

Heuristic Algorithm ◽

Spontaneous Reporting ◽

Adverse Drug Events ◽

Information Loss ◽

New Method ◽

Data Publishing ◽

Equivalence Group ◽

Private Data ◽

Reporting Systems

Spontaneous reporting systems (SRSs) are used to collect adverse drug events (ADEs) for their evaluation and analysis. Periodical SRS data publication gives rise to a problem where sensitive, private data can be discovered through various attacks. The existing SRS data publishing methods are vulnerable to Medicine Discontinuation Attack(MD-attack) and Substantial symptoms-attack(SS-attack). To remedy this problem, an improved periodical SRS data publishing—PPMS(k, θ, ɑ)-bounding is proposed. This new method can recognize MD-attack by ensuring that each equivalence group contains at least k new medicine discontinuation records. The SS-attack can be thwarted using a heuristic algorithm. Theoretical analysis indicates that PPMS(k, θ, ɑ)-bounding can thwart the above-mentioned attacks. The experimental results also demonstrate that PPMS(k, θ, ɑ)-bounding can provide much better protection for privacy than the existing method and the new method dose not increase the information loss. PPMS(k, θ, ɑ)-bounding can improve the privacy, guaranteeing the information usability of the released tables.

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