اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدRefining Adverse Drug Reactions using Association Rule Mining for Electronic Healthcare Data
142
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. when an association between two variables is identified due to them both being associated to a third variable). In this paper we propose a proof of concept method that learns common associations and uses this knowledge to automatically refine side effect signals (i.e. exposure-outcome associations) by removing instances of the exposure-outcome associations that are caused by confounding. This leaves the signal instances that are most likely to correspond to true side effect occurrences. We then calculate a novel measure termed the confounding-adjusted risk value, a more accurate absolute risk value of a patient experiencing the outcome within 60 days of the exposure. Tentative results suggest that the method works. For the four signals (i.e. exposure-outcome associations) investigated we are able to correctly filter the majority of exposure-outcome instances that were unlikely to correspond to true side effects. The method is likely to improve when tuning the association rule mining parameters for specific health outcomes. This paper shows that it may be possible to filter signals at a patient level based on association rules learned from considering patients medical histories. However, additional work is required to develop a way to automate the tuning of the methods parameters.
قيم البحث
اقرأ أيضاً
Purpose: To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction signals obtained via longitudinal observational data. Methods: We con
sidered six drug families that are commonly associated with myocardial infarction in observational healthcare data, but where the causal relationship ground truth is known (adverse drug reaction or not). We applied emergent pattern mining to find itemsets of drugs and medical events that are associated with the development of myocardial infarction. These are the candidate confounding interaction terms. We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms. Results The methodology was able to account for signals generated due to confounding and a cox regression with elastic net regularisation correctly ranked the drug families known to be true adverse drug reactions above those.
The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare dat
abase containing medical information on over 11 million patients that has excellent potential for detecting ADRs. In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families. This is the first comparison of ADR signalling algorithms that includes MUTARA and HUNT and enabled us to set a benchmark for the adverse drug reaction signalling ability of the THIN database. The drugs were selectively chosen to enable a comparison with previous work and for variety. It was found that no algorithm was generally superior and the algorithms natural thresholds act at variable stringencies. Furthermore, none of the algorithms perform well at detecting rare ADRs.
The work presented in this paper is part of the cooperative research project AUTO-OPT carried out by twelve partners from the automotive industries. One major work package concerns the application of data mining methods in the area of automotive desi
gn. Suitable methods for data preparation and data analysis are developed. The objective of the work is the re-use of data stored in the crash-simulation department at BMW in order to gain deeper insight into the interrelations between the geometric variations of the car during its design and its performance in crash testing. In this paper a method for data analysis of finite element models and results from crash simulation is proposed and application to recent data from the industrial partner BMW is demonstrated. All necessary steps from data pre-processing to re-integration into the working environment of the engineer are covered.
Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. There is no specific drug for COVID-19, highlighting the urgent need for the development of effective therapeutics. T
o identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating the gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from mild and severe COVID-19 patients. We identified 281 FDA-approved drugs that have the potential to be effective against SARS-CoV-2 infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus. In conclusion, we have identified a list of repurposable anti-SARS-CoV-2 drugs using a systems biology approach.
With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from
limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority.
الأسئلة المقترحة
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
