10,012 research outputs found
Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs
are one of most common causes to withdraw some drugs from market. Prescription
event monitoring (PEM) is an important approach to detect the adverse drug
reactions. The main problem to deal with this method is how to automatically
extract the medical events or side effects from high-throughput medical events,
which are collected from day to day clinical practice. In this study we propose
a novel concept of feature matrix to detect the ADRs. Feature matrix, which is
extracted from big medical data from The Health Improvement Network (THIN)
database, is created to characterize the medical events for the patients who
take drugs. Feature matrix builds the foundation for the irregular and big
medical data. Then feature selection methods are performed on feature matrix to
detect the significant features. Finally the ADRs can be located based on the
significant features. The experiments are carried out on three drugs:
Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug
are detected and better performance is achieved compared to other computerized
methods. The detected ADRs are based on computerized methods, further
investigation is needed.Comment: International Journal of Information Technology and Computer Science
(IJITCS), in print, 201
Detect adverse drug reactions for drug Atorvastatin
Adverse drug reactions (ADRs) are big concern for public health. ADRs are one
of most common causes to withdraw some drugs from markets. Now two major
methods for detecting ADRs are spontaneous reporting system (SRS), and
prescription event monitoring (PEM). The World Health Organization (WHO)
defines a signal in pharmacovigilance as "any reported information on a
possible causal relationship between an adverse event and a drug, the
relationship being unknown or incompletely documented previously". For
spontaneous reporting systems, many machine learning methods are used to detect
ADRs, such as Bayesian confidence propagation neural network (BCPNN), decision
support methods, genetic algorithms, knowledge based approaches, etc. One
limitation is the reporting mechanism to submit ADR reports, which has serious
underreporting and is not able to accurately quantify the corresponding risk.
Another limitation is hard to detect ADRs with small number of occurrences of
each drug-event association in the database. In this paper we propose feature
selection approach to detect ADRs from The Health Improvement Network (THIN)
database. First a feature matrix, which represents the medical events for the
patients before and after taking drugs, is created by linking patients'
prescriptions and corresponding medical events together. Then significant
features are selected based on feature selection methods, comparing the feature
matrix before patients take drugs with one after patients take drugs. Finally
the significant ADRs can be detected from thousands of medical events based on
corresponding features. Experiments are carried out on the drug Atorvastatin.
Good performance is achieved.Comment: Fifth International Symposium on Computational Intelligence and
Design (ISCID), 213-216, 2012. arXiv admin note: substantial text overlap
with arXiv:1308.514
Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data
Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal observational data are often non-causal and occur due to confounding.
The focus of this paper is to investigate incorporating information from additional databases to complement the longitudinal observational database analysis. We investigate the detection of prescription drug side effects as this is an example of a causal relationship. In previous work a framework was proposed for detecting side effects only using longitudinal data. In this paper we combine a measure of association derived from mining a spontaneous reporting system database to previously proposed analysis that extracts domain expertise features for causal analysis of a UK general practice longitudinal database.
The results show that there is a significant improvement to the performance of detecting prescription drug side effects when the longitudinal observation data analysis is complemented by incorporating additional drug safety sources into the framework. The area under the receiver operating characteristic curve (AUC) for correctly classifying a side effect when other data were considered was 0.967, whereas without it the AUC was 0.923 However, the results of this paper may be biased by the evaluation and future work should overcome this by developing an unbiased reference set
A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations
Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data.
In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill’s causality considerations to automate the Bradford Hill’s causality assessment.
We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership’s non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data
Signalling paediatric side effects using an ensemble of simple study designs
Background: Children are frequently prescribed medication `o-label', meaning there has not been sucient testing of the medication to determine its safety or eectiveness. The main reason this safety knowledge is lacking is due to
ethical restrictions that prevent children from being included in the majority of clinical trials.
Methods: Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classifier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classier is trained using known adverse drug reactions or known non-adverse drug reaction relationships.
Results: The novel ensemble framework obtained a false positive rate of 0:149, a sensitivity of 0:547 and a specificity of 0:851 when implemented on a reference set
of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design.
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions eectively
- …