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Michael P. Epstein - One of the best experts on this subject based on the ideXlab platform.
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Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies.
Scientific reports, 2019Co-Authors: Aaron M. Holleman, K. Alaine Broadaway, Richard Duncan, Andrei Todor, Lynn M. Almli, Bekh Bradley, Kerry J. Ressler, Debashis Ghosh, Jennifer G. Mulle, Michael P. EpsteinAbstract:Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom Data from Questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate Questionnaire Data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom Data collectively using a statistical framework that compares similarity in multivariate symptom-scale Data from Questionnaires to similarity in common genetic variants across a gene. We use simulated Data to demonstrate this strategy provides substantially increased power over standard approaches that collapse Questionnaire Data into a single surrogate outcome. We also illustrate our approach using GWAS Data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom Data of arbitrary dimension.
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Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies
2018Co-Authors: Aaron M. Holleman, K. Alaine Broadaway, Richard Duncan, Lynn M. Almli, Bekh Bradley, Kerry J. Ressler, Debashis Ghosh, Jennifer G. Mulle, Michael P. EpsteinAbstract:Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom Data from Questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate Questionnaire Data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom Data collectively using a statistical framework that compares similarity in multivariate symptom-scale Data from Questionnaires to similarity in common genetic variants across a gene. We use simulated Data to demonstrate this strategy provides substantially increased power over standard approaches that collapse Questionnaire Data into a single surrogate outcome. We also illustrate our approach using GWAS Data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom Data of arbitrary dimension (thereby aligning with National Institute of Mental Health9s Research Domain Criteria).
Takeshi Furuhashi - One of the best experts on this subject based on the ideXlab platform.
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FUZZ-IEEE - A study on extraction of minority groups in Questionnaire Data based on spectral clustering
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014Co-Authors: Kazuto Inagaki, Tomohiro Yoshikawa, Takeshi FuruhashiAbstract:In the field of marketing, a Questionnaire is one of the most important approaches in order to research the market or to design a marketing strategy. On the other hand, people have a variety of individuality recently, then respondents have various impressions on evaluation objects. In the analysis of collected Questionnaire Data, it is important not only to analyze overall trends but also to discover minority groups which have strong impressions but are different from general groups. It is, however, difficult to extract minority groups by conventional cluster analysis applied to Questionnaire Data, because they generally aim at extracting majority groups or making a rough clustering. In this paper, we propose the extraction method of minority groups in Questionnaire Data using the spectral clustering method which considers local similarity and extracts the clusters having less connection to general groups.
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FUZZ-IEEE - Study on analysis of Questionnaire Data based on interactive clustering
2009 IEEE International Conference on Fuzzy Systems, 2009Co-Authors: Yosuke Watanabe, Tomohiro Yoshikawa, Takeshi FuruhashiAbstract:Recently, several kinds of values have been employed with respect to the diversification of individuality in the market. Some of these values are currently supported by only a few people, who are referred to as a “minority group”. However, there is the possibility that such groups will grow into majority groups with changes in historical background or people's sensitivity. It is both important and effective for market analysis to determine these minority groups at an early stage. Companies often employ Questionnaires to develop marketing strategies or design new products, which offer a chance to determine these minority groups. With conventional methods, respondents to a Questionnaire are classified based on such attributes as gender and age, and then the classified groups are analyzed or compared. Although conventional analysis is effective for grasping the overall tendency of the evaluation Data, it is difficult to determine minority groups because of the diversity of individuality. On the other hand, we have proposed clustering methods based on the tendencies of the answers to the Questionnaire. This paper proposes a new method for visualizing the evaluated Data based on both the obtained values and their correlation with cluster respondents interactively in the visible space. This paper applies the proposed method to web Questionnaire Data and shows that an analysis of the results effectively assists us to determine minority groups.
Antanas Verikas - One of the best experts on this subject based on the ideXlab platform.
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Random forests based monitoring of human larynx using Questionnaire Data
Expert Systems with Applications, 2012Co-Authors: Marija Bacauskiene, Antanas Verikas, Adas Gelzinis, Aurelija VegieneAbstract:This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject's Questionnaire Data. By applying random forests (RF), Questionnaire Data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important Questionnaire statements are determined using RF variable importance evaluations. To explore Data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF Data proximity matrix. When testing the developed tools on a set of Data collected from 109 subjects, the 100% classification accuracy was obtained on unseen Data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the Data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of Data and facilitate Data exploration aimed at identifying subjects belonging to a ''risk group''. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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ISDA - Monitoring human larynx by random forests using Questionnaire Data
2011 11th International Conference on Intelligent Systems Design and Applications, 2011Co-Authors: Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Virgilijus UlozaAbstract:This paper is concerned with noninvasive monitoring of human larynx using subject's Questionnaire Data. By applying random forests (RF), Questionnaire Data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important Questionnaire statements are determined using RF variable importance evaluations. To explore multidimensional Data, t-Distributed Stochastic Neighbor Embedding (t-SNE) and multidimensional scaling (MDS) are applied to the RF Data proximity matrix. When testing the developed tools on a set of Data collected from 109 subjects, 100% classification accuracy was obtained on unseen Data coming from two — healthy and pathological — classes. The accuracy of 80.7% was achieved when classifying the Data into the healthy, cancerous, and noncancerous classes. The t-SNE and MDS mapping techniques facilitate Data exploration aimed at identifying subjects belonging to a ”risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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Combining image, voice, and the patient's Questionnaire Data to categorize laryngeal disorders
Artificial intelligence in medicine, 2010Co-Authors: Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Virgilijus Uloza, Magnus Hållander, Marius KasetaAbstract:Objective: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and Questionnaire Data. Methods: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the Questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query Data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search. Results: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set Data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and Questionnaire Data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice Data, respectively. The test set Data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging. Conclusions: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced Data sets used, the error rates obtained for the three classes were rather similar.
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Using the patient's Questionnaire Data to screen laryngeal disorders
Computers in biology and medicine, 2009Co-Authors: Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Virgilijus Uloza, Marius KasetaAbstract:This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's Questionnaire Data. By applying the genetic search, the most important Questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the Questionnaire Data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as Questionnaire statements is applied. When testing the developed tools on the set of Data collected from 180 patients, the classification accuracy of 85.0% was obtained. Bearing in mind the subjective nature of the Data, the obtained classification accuracy is rather encouraging. The CCA allows obtaining ordered two-dimensional maps of the Data in various spaces and facilitates the exploration of automated decisions provided by the system and determination of relevant groups of patients for various comparisons.
Marija Bacauskiene - One of the best experts on this subject based on the ideXlab platform.
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Random forests based monitoring of human larynx using Questionnaire Data
Expert Systems with Applications, 2012Co-Authors: Marija Bacauskiene, Antanas Verikas, Adas Gelzinis, Aurelija VegieneAbstract:This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject's Questionnaire Data. By applying random forests (RF), Questionnaire Data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important Questionnaire statements are determined using RF variable importance evaluations. To explore Data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF Data proximity matrix. When testing the developed tools on a set of Data collected from 109 subjects, the 100% classification accuracy was obtained on unseen Data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the Data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of Data and facilitate Data exploration aimed at identifying subjects belonging to a ''risk group''. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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ISDA - Monitoring human larynx by random forests using Questionnaire Data
2011 11th International Conference on Intelligent Systems Design and Applications, 2011Co-Authors: Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Virgilijus UlozaAbstract:This paper is concerned with noninvasive monitoring of human larynx using subject's Questionnaire Data. By applying random forests (RF), Questionnaire Data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important Questionnaire statements are determined using RF variable importance evaluations. To explore multidimensional Data, t-Distributed Stochastic Neighbor Embedding (t-SNE) and multidimensional scaling (MDS) are applied to the RF Data proximity matrix. When testing the developed tools on a set of Data collected from 109 subjects, 100% classification accuracy was obtained on unseen Data coming from two — healthy and pathological — classes. The accuracy of 80.7% was achieved when classifying the Data into the healthy, cancerous, and noncancerous classes. The t-SNE and MDS mapping techniques facilitate Data exploration aimed at identifying subjects belonging to a ”risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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Combining image, voice, and the patient's Questionnaire Data to categorize laryngeal disorders
Artificial intelligence in medicine, 2010Co-Authors: Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Virgilijus Uloza, Magnus Hållander, Marius KasetaAbstract:Objective: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and Questionnaire Data. Methods: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the Questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query Data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search. Results: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set Data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and Questionnaire Data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice Data, respectively. The test set Data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging. Conclusions: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced Data sets used, the error rates obtained for the three classes were rather similar.
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Using the patient's Questionnaire Data to screen laryngeal disorders
Computers in biology and medicine, 2009Co-Authors: Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Virgilijus Uloza, Marius KasetaAbstract:This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's Questionnaire Data. By applying the genetic search, the most important Questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the Questionnaire Data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as Questionnaire statements is applied. When testing the developed tools on the set of Data collected from 180 patients, the classification accuracy of 85.0% was obtained. Bearing in mind the subjective nature of the Data, the obtained classification accuracy is rather encouraging. The CCA allows obtaining ordered two-dimensional maps of the Data in various spaces and facilitates the exploration of automated decisions provided by the system and determination of relevant groups of patients for various comparisons.
Steinar Tretli - One of the best experts on this subject based on the ideXlab platform.
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Estimating mean sojourn time and screening sensitivity using Questionnaire Data on time since previous screening.
Journal of medical screening, 2008Co-Authors: Harald Weedon-fekjær, Bo Henry Lindqvist, Lars J. Vatten, Odd O. Aalen, Steinar TretliAbstract:ObjectivesMean sojourn time (MST) and screening test sensitivity (STS), is usually estimated by Markov models using incidence Data from the first screening round and the interval between screening examinations. However, several screening programmes do not have full registration of cancers submerging after screening, and increased use of opportunistic screening over time can raise questions regarding the quality of interval cancer registration.Methods/settingsBased on the earlier used Markov model, formulas for expected number of cases given time since former screening activity was developed. Using Questionnaire Data for 336,533 women in the Norwegian Breast Cancer Screening Programme (NBCSP), mean square regression estimates of MST and STS were calculated.ResultsIn contrast to the previously used method, the new approach gave satisfactory model fit. MST was estimated to 5.6 years for women aged 50–59 years, and 6.9 years for women aged 60–69 years, and STS was estimated to 55% and 60%, respectively. Attem...
