The Experts below are selected from a list of 3243 Experts worldwide ranked by ideXlab platform
Niklas G Noren - One of the best experts on this subject based on the ideXlab platform.
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Outlier Removal to uncover patterns in adverse drug reaction surveillance a simple unmasking strategy
Pharmacoepidemiology and Drug Safety, 2013Co-Authors: Kristina Juhlin, Kristina Star, Niklas G NorenAbstract:PurposeThis study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential Outliers in two spontaneous reporting databases and evaluate the impact of Outlier Removal on disproportionality analysis. MethodsWe propose an algorithm that identifies influential Outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of Outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China. ResultsFor WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia Outlier Removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential Outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an Outlier. The overall increase in the number of SDRs for both datasets was 3%. ConclusionMasking by Outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
Kristina Star - One of the best experts on this subject based on the ideXlab platform.
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Outlier Removal Expedites Adverse Drug Reaction Surveillance - Evaluation of a Simple Unmasking Strategy
Pharmacoepidemiology and Drug Safety, 2014Co-Authors: Kristina Juhlin, Kristina Star, G. Niklas NorénAbstract:Outlier Removal Expedites Adverse Drug Reaction Surveillance - Evaluation of a Simple Unmasking Strategy
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Outlier Removal to uncover patterns in adverse drug reaction surveillance a simple unmasking strategy
Pharmacoepidemiology and Drug Safety, 2013Co-Authors: Kristina Juhlin, Kristina Star, Niklas G NorenAbstract:PurposeThis study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential Outliers in two spontaneous reporting databases and evaluate the impact of Outlier Removal on disproportionality analysis. MethodsWe propose an algorithm that identifies influential Outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of Outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China. ResultsFor WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia Outlier Removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential Outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an Outlier. The overall increase in the number of SDRs for both datasets was 3%. ConclusionMasking by Outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
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Outlier Removal to uncover patterns in adverse drug reaction surveillance – a simple unmasking strategy
Pharmacoepidemiology and Drug Safety, 2013Co-Authors: Kristina Juhlin, Kristina Star, G. Niklas NorénAbstract:PurposeThis study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential Outliers in two spontaneous reporting databases and evaluate the impact of Outlier Removal on disproportionality analysis. MethodsWe propose an algorithm that identifies influential Outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of Outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China. ResultsFor WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia Outlier Removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential Outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an Outlier. The overall increase in the number of SDRs for both datasets was 3%. ConclusionMasking by Outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
Hanzi Wang - One of the best experts on this subject based on the ideXlab platform.
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conceptual space based gross Outlier Removal for geometric model fitting
International Conference on Control Automation Robotics and Vision, 2016Co-Authors: Xing Wang, Jin Zheng, Guobao Xiao, Yan Yan, Hanzi WangAbstract:In this paper, we propose an efficient and robust gross Outlier Removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross Outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and gross Outliers are significantly different. Specifically, inliers of each model instance are distributed in a subspace and they are far away from the origin of the conceptual space, while gross Outliers are distributed near the origin. In this manner, the problem of densely gross Outlier Removal is formulated as a binary classification problem. The main advantage of the proposed method is that it can handle data with a large proportion of Outliers and effectively remove gross Outliers in data. Experimental results on both synthetic and real data have demonstrated the efficiency and effectiveness of the proposed method.
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ICARCV - Conceptual space based gross Outlier Removal for geometric model fitting
2016 14th International Conference on Control Automation Robotics and Vision (ICARCV), 2016Co-Authors: Xing Wang, Jin Zheng, Guobao Xiao, Yan Yan, Hanzi WangAbstract:In this paper, we propose an efficient and robust gross Outlier Removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross Outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and gross Outliers are significantly different. Specifically, inliers of each model instance are distributed in a subspace and they are far away from the origin of the conceptual space, while gross Outliers are distributed near the origin. In this manner, the problem of densely gross Outlier Removal is formulated as a binary classification problem. The main advantage of the proposed method is that it can handle data with a large proportion of Outliers and effectively remove gross Outliers in data. Experimental results on both synthetic and real data have demonstrated the efficiency and effectiveness of the proposed method.
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an Outlier Removal method by statistically analyzing hypotheses for geometric model fitting
International Conference on Image and Graphics, 2015Co-Authors: Guobao Xiao, Hanzi WangAbstract:In this paper, we propose an Outlier Removal method which utilizes the information of hypotheses for model fitting. The proposed method statistically analyzes the properties of data points in two groups of hypotheses, i.e., “good hypotheses” and “bad hypotheses”. We show that the bad hypotheses, whose parameters are far from the parameters of model instances in data, also contain the correlation information between data points. The information can be used to effectively remove Outliers from the data. Experimental results show the proposed method can effectively remove Outliers on real datasets.
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ICIG (1) - An Outlier Removal Method by Statistically Analyzing Hypotheses for Geometric Model Fitting
Lecture Notes in Computer Science, 2015Co-Authors: Guobao Xiao, Hanzi WangAbstract:In this paper, we propose an Outlier Removal method which utilizes the information of hypotheses for model fitting. The proposed method statistically analyzes the properties of data points in two groups of hypotheses, i.e., “good hypotheses” and “bad hypotheses”. We show that the bad hypotheses, whose parameters are far from the parameters of model instances in data, also contain the correlation information between data points. The information can be used to effectively remove Outliers from the data. Experimental results show the proposed method can effectively remove Outliers on real datasets.
Augusto Sarti - One of the best experts on this subject based on the ideXlab platform.
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A Geometrical–Statistical Approach to Outlier Removal for TDOA Measurements
IEEE Transactions on Signal Processing, 2017Co-Authors: Marco Compagnoni, Alessia Pini, Antonio Canclini, Paolo Bestagini, Fabio Antonacci, Stefano Tubaro, Augusto SartiAbstract:The curse of Outlier measurements in estimation problems is a well-known issue in a variety of fields. Therefore, Outlier Removal procedures, which enables the identification of spurious measurements within a set, have been developed for many different scenarios and applications. In this paper, we propose a statistically motivated Outlier Removal algorithm for time differences of arrival (TDOAs), or equivalently range differences (RD), acquired at sensor arrays. The method exploits the TDOA-space formalism and works by only knowing relative sensor positions. As the proposed method is completely independent from the application for which measurements are used, it can be reliably used to identify Outliers within a set of TDOA/RD measurements in different fields (e.g., acoustic source localization, sensor synchronization, radar, remote sensing, etc.). The proposed Outlier Removal algorithm is validated by means of synthetic simulations and real experiments.
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a geometrical statistical approach to Outlier Removal for tdoa measurements
IEEE Transactions on Signal Processing, 2017Co-Authors: Marco Compagnoni, Alessia Pini, Antonio Canclini, Paolo Bestagini, Fabio Antonacci, Stefano Tubaro, Augusto SartiAbstract:The curse of Outlier measurements in estimation problems is a well-known issue in a variety of fields. Therefore, Outlier Removal procedures, which enables the identification of spurious measurements within a set, have been developed for many different scenarios and applications. In this paper, we propose a statistically motivated Outlier Removal algorithm for time differences of arrival (TDOAs), or equivalently range differences (RD), acquired at sensor arrays. The method exploits the TDOA-space formalism and works by only knowing relative sensor positions. As the proposed method is completely independent from the application for which measurements are used, it can be reliably used to identify Outliers within a set of TDOA/RD measurements in different fields (e.g., acoustic source localization, sensor synchronization, radar, remote sensing, etc.). The proposed Outlier Removal algorithm is validated by means of synthetic simulations and real experiments.
Kristina Juhlin - One of the best experts on this subject based on the ideXlab platform.
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Outlier Removal Expedites Adverse Drug Reaction Surveillance - Evaluation of a Simple Unmasking Strategy
Pharmacoepidemiology and Drug Safety, 2014Co-Authors: Kristina Juhlin, Kristina Star, G. Niklas NorénAbstract:Outlier Removal Expedites Adverse Drug Reaction Surveillance - Evaluation of a Simple Unmasking Strategy
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Outlier Removal to uncover patterns in adverse drug reaction surveillance a simple unmasking strategy
Pharmacoepidemiology and Drug Safety, 2013Co-Authors: Kristina Juhlin, Kristina Star, Niklas G NorenAbstract:PurposeThis study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential Outliers in two spontaneous reporting databases and evaluate the impact of Outlier Removal on disproportionality analysis. MethodsWe propose an algorithm that identifies influential Outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of Outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China. ResultsFor WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia Outlier Removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential Outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an Outlier. The overall increase in the number of SDRs for both datasets was 3%. ConclusionMasking by Outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
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Outlier Removal to uncover patterns in adverse drug reaction surveillance – a simple unmasking strategy
Pharmacoepidemiology and Drug Safety, 2013Co-Authors: Kristina Juhlin, Kristina Star, G. Niklas NorénAbstract:PurposeThis study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential Outliers in two spontaneous reporting databases and evaluate the impact of Outlier Removal on disproportionality analysis. MethodsWe propose an algorithm that identifies influential Outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of Outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China. ResultsFor WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia Outlier Removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential Outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an Outlier. The overall increase in the number of SDRs for both datasets was 3%. ConclusionMasking by Outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
