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- Bate, Andrew
(författare)
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The use of Bayesian confidence propagation neural network in pharmacovigilance
- 2003
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously."The analysis of such a large amount of data on a case by case basis is impossible with the resources available. Therefore a quantitative, data mining procedure has been developed to improve the focus of the clinical signal detection process. The method used, is referred to as the BCPNN (Bayesian Confidence Propagation Neural Network). This not only assists in the early detection of adverse drug reactions (ADRs) but also further analysis of such signals. The method uses Bayesian statistical principles to quantify apparent dependencies in the data set. This quantifies the degree to which a specific drug- ADR combination is different from a background (in this case the WHO database). The measure of disproportionality used, is referred to as the Information Component (IC) because of its' origins in Information Theory. A confidence interval is calculated for the IC of each combination. A neural network approach allows all drug-ADR combinations in the database to be analysed in an automated manner. Evaluations of the effectiveness of the BCPNN in signal detection are described.To compare how a drug association compares in unexpectedness to related drugs, which might be used for the same clinical indication, the method is extended to consideration of groups of drugs. The benefits and limitations of this approach are discussed with examples of known group effects (ACE inhibitors - coughing and antihistamines - heart rate and rhythm disorders.) An example of a clinically important, novel signal found using the BCPNN approach is also presented. The signal of antipsychotics linked with heart muscle disorder was detected using the BCPNN and reported.The BCPNN is now routinely used in signal detection to search single drug - single ADR combinations. The extension of the BCPNN to discover 'unexpected' complex dependencies between groups of drugs and adverse reactions is described. A recurrent neural network method has been developed for finding complex patterns in incomplete and noisy data sets. The method is demonstrated on an artificial test set. Implementation on real data is demonstrated by examining the pattern of adverse reactions highlighted for the drug haloperidol. Clinically important, complex relationships in this kind of data are previously unexplored.The BCPNN method has been shown and tested for use in routine signal detection, refining signals and in finding complex patterns. The usefulness of the output is influenced by the quality of the data in the database. Therefore, this method should be used to detect, rather than evaluate signals. The need for clinical analyses of case series remains crucial.
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- Burkimsher, Andrew, et al.
(författare)
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A survey of scheduling metrics and an improved ordering policy for list schedulers operating on workloads with dependencies and a wide variation in execution times
- 2012
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Ingår i: Future Generation Computer Systems. - : North-Holland. - 0167-739X .- 1872-7115. ; 29:8, s. 2009-2025
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers the dynamic scheduling of parallel, dependent tasks onto a static, distributed computing platform, with the intention of delivering fairness and quality of service (QoS) to users. The key QoS requirement is that responsiveness is maintained for workloads with a wide range of execution times (minutes to months) even under transient periods of overload. A survey of schedule QoS metrics is presented, classified into those dealing with responsiveness, fairness and utilisation. These metrics are evaluated as to their ability to detect undesirable features of schedules. The Schedule Length Ratio (SLR) metric is shown to be the most helpful for measuring responsiveness in the presence of dependencies. A novel list scheduling policy called Projected-SLR is presented that delivers good responsiveness and fairness by using the SLR metric in its scheduling decisions. Projected-SLR is found to perform equally as well in responsiveness, fairness and utilisation as the best of the other scheduling policies evaluated (Shortest Remaining Time First/SRTF), using synthetic workloads and an industrial trace. However, Projected-SLR does this with a guarantee of starvation-free behaviour, unlike SRTF.
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- Caster, Ola, et al.
(författare)
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Large-scale regression-based pattern discovery : The example of screening the WHO global drug safety database
- 2010
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Ingår i: Statistical Analysis and Data Mining. - : Wiley. - 1932-1864 .- 1932-1872. ; 3:4, s. 197-208
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Tidskriftsartikel (refereegranskat)abstract
- Most measures of interestingness for patterns of co-occurring events are based on data projections onto contingency tables for the events of primary interest. As an alternative, this article presents the first implementation of shrinkage logistic regression for large-scale pattern discovery, with an evaluation of its usefulness in real-world binary transaction data. Regression accounts for the impact of other covariates that may confound or otherwise distort associations. The application considered is international adverse drug reaction (ADR) surveillance, in which large collections of reports on suspected ADRs are screened for interesting reporting patterns worthy of clinical follow-up. Our results show that regression-based pattern discovery does offer practical advantages. Specifically it can eliminate false positives and false negatives due to other covariates. Furthermore, it identifies some established drug safety issues earlier than a measure based on contingency tables. While regression offers clear conceptual advantages, our results suggest that methods based on contingency tables will continue to play a key role in ADR surveillance, for two reasons: the failure of regression to identify some established drug safety concerns as early as the currently used measures, and the relative lack of transparency of the procedure to estimate the regression coefficients. This suggests shrinkage regression should be used in parallel to existing measures of interestingness in ADR surveillance and other large-scale pattern discovery applications.
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- Dong, Guojun, et al.
(författare)
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Optimizing Signal Management in a Vaccine Adverse Event Reporting System : A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing
- 2024
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Ingår i: Drug Safety. - : Adis. - 0114-5916 .- 1179-1942. ; 47:2, s. 173-
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as ‘statistical alerts’) generated is expected. Objectives: The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept. Methods: The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+. Results: Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs. Conclusion: Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management. © 2023, The Author(s).
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- Hopstadius, Johan, et al.
(författare)
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Impact of Stratification on Adverse Drug Reaction Surveillance
- 2008
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Ingår i: Drug Safety. - 0114-5916 .- 1179-1942. ; 31:11, s. 1035-1048
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND OBJECTIVES: Automated screening for excessive adverse drug reaction (ADR) reporting rates has proven useful as a tool to direct clinical review in large-scale drug safety signal detection. Some measures of disproportionality can be adjusted to eliminate any undue influence on the ADR reporting rate of covariates, such as patient age or country of origin, by using a weighted average of stratum-specific measures of disproportionality. Arguments have been made in favour of routine adjustment for a set of common potential confounders using stratification. The aim of this paper is to investigate the impact of using adjusted observed-to-expected ratios, as implemented for the Empirical Bayes Geometric Mean (EBGM) and the information component (IC) measures of disproportionality, for first-pass analysis of the WHO database. METHODS: A simulation study was carried out to investigate the impact of simultaneous adjustment for several potential confounders based on stratification. Comparison between crude and adjusted observed-to-expected ratios were made based on random allocation of reports to a set of strata with a realistic distribution of stratum sizes. In a separate study, differences between the crude IC value and IC values adjusted for (combinations of) patient sex, age group, reporting quarter and country of origin, with respect to their concordance with a literature comparison were analysed. Comparison was made to the impact on signal detection performance of a triage criterion requiring reports from at least two countries before a drug-ADR pair was highlighted for clinical review. RESULTS: The simulation study demonstrated a clear tendency of the adjusted observed-to-expected ratio to spurious (and considerable) underestimation relative to the crude one, in the presence of any very small strata in a stratified database. With carefully implemented stratification that did not yield any very small strata, this tendency could be avoided. Routine adjustment for potential confounders improved signal detection performance relative to the literature comparison, but the magnitude of the improvement was modest. The improvement from the triage criterion was more considerable. DISCUSSION AND CONCLUSIONS: Our results indicate that first-pass screening based on observed-to-expected ratios adjusted with stratification may lead to missed signals in ADR surveillance, unless very small strata are avoided. In addition, the improvement in signal detection performance due to routine adjustment for a set of common confounders appears to be smaller than previously assumed. Other approaches to improving signal detection performance such as the development of refined triage criteria may be more promising areas for future research.
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- Hägg, Staffan, 1963-, et al.
(författare)
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Associations between venous thromboembolism and antipsychotics : A study of the WHO database of adverse drug reactions
- 2008
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Ingår i: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 31:8, s. 685-694
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Tidskriftsartikel (refereegranskat)abstract
- Background: Concern has been raised about the occurrence of venous thromboembolism (VTE) during treatment with antipsychotics. However, to date, clozapine is the only antipsychotic agent for which recurring evidence supports an association with VTE. Therefore, the aim of this study was to investigate the association between antipsychotic drugs, including clozapine and VTE. Study design and methods: Data mining of the WHO database of adverse drug reactions (ADRs) using Bayesian statistics is in routine use for early alerting to possible ADRs. An information component measure was used to investigate the association between antipsychotic drugs and VTE reactions in the database. Results: A total of 754 suspected cases of VTE related to treatment with antipsychotics had been reported. After excluding cases related to clozapine, 379 cases remained. A robust association was found for the second-generation antipsychotics group but not for the high-potency, first-generation antipsychotics group or the low-potency first-generation antipsychotics group. The individual compounds with statistically significant associations were olanzapine, sertindole and zuclopenthixol. A time-dependent analysis showed that the associations were positive for these drugs in 2002, 2001 and 2003, respectively. Case analyses were undertaken after excluding ten suspected duplicate reports. Of the remaining 369 cases, 91 cases were associated with olanzapine, 9 with zuclopenthixol and 6 with sertindole. Conclusions: VTE was more often reported with the antipsychotic drugs olanzapine, sertindole and zuclopenthixol than with other drugs in the WHO database. Further studies are warranted to explain this disproportional reporting. Since the associations found were based on incomplete clinical data, the results should be considered as preliminary and interpreted cautiously. © 2008 Adis Data Information BV. All rights reserved.
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