The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Bob Stienen - One of the best experts on this subject based on the ideXlab platform.
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spot open source framework for scientific data repository and interactive visualization
SoftwareX, 2019Co-Authors: Faruk Diblen, Jisk Attema, Rena Bakhshi, S Caron, Luc Hendriks, Bob StienenAbstract:Abstract spot is an open source and free visual data Analytics Tool for multi-dimensional data-sets. Its web-based interface enables user to do a quick and interactive analysis of complex data. Various operations on data are implemented such as aggregation and filtering. The interface supports OpenGL acceleration, which makes the generated charts very responsive. In order to have scalability, the software also supports PostgreSQL as its database. It follows FAIR principles to allow reuse and comparison of the published data-sets.
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spot open source framework for scientific data repository and interactive visualization
arXiv: Human-Computer Interaction, 2018Co-Authors: Faruk Diblen, Jisk Attema, Rena Bakhshi, S Caron, Luc Hendriks, Bob StienenAbstract:SPOT is an open source and free visual data Analytics Tool for multi-dimensional data-sets. Its web-based interface allows a quick analysis of complex data interactively. The operations on data such as aggregation and filtering are implemented. The generated charts are responsive and OpenGL supported. It follows FAIR principles to allow reuse and comparison of the published data-sets. The software also support PostgreSQL database for scalability.
Hans-christoph Pape - One of the best experts on this subject based on the ideXlab platform.
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validation of a visual based Analytics Tool for outcome prediction in polytrauma patients watson trauma pathway explorer and comparison with the predictive values of triss
Journal of Clinical Medicine, 2021Co-Authors: C Niggli, Hans-christoph Pape, Philipp Niggli, Ladislav MicaAbstract:Introduction: Big data-based artificial intelligence (AI) has become increasingly important in medicine and may be helpful in the future to predict diseases and outcomes. For severely injured patients, a new Analytics Tool has recently been developed (WATSON Trauma Pathway Explorer) to assess individual risk profiles early after trauma. We performed a validation of this Tool and a comparison with the Trauma and Injury Severity Score (TRISS), an established trauma survival estimation score. Methods: Prospective data collection, level I trauma centre, 1 January 2018–31 December 2019. Inclusion criteria: Primary admission for trauma, injury severity score (ISS) ≥ 16, age ≥ 16. Parameters: Age, ISS, temperature, presence of head injury by the Glasgow Coma Scale (GCS). Outcomes: SIRS and sepsis within 21 days and early death within 72 h after hospitalisation. Statistics: Area under the receiver operating characteristic (ROC) curve for predictive quality, calibration plots for graphical goodness of fit, Brier score for overall performance of WATSON and TRISS. Results: Between 2018 and 2019, 107 patients were included (33 female, 74 male; mean age 48.3 ± 19.7; mean temperature 35.9 ± 1.3; median ISS 30, IQR 23–36). The area under the curve (AUC) is 0.77 (95% CI 0.68–0.85) for SIRS and 0.71 (95% CI 0.58–0.83) for sepsis. WATSON and TRISS showed similar AUCs to predict early death (AUC 0.90, 95% CI 0.79–0.99 vs. AUC 0.88, 95% CI 0.77–0.97; p = 0.75). The goodness of fit of WATSON (X2 = 8.19, Hosmer–Lemeshow p = 0.42) was superior to that of TRISS (X2 = 31.93, Hosmer–Lemeshow p < 0.05), as was the overall performance based on Brier score (0.06 vs. 0.11 points). Discussion: The validation supports previous reports in terms of feasibility of the WATSON Trauma Pathway Explorer and emphasises its relevance to predict SIRS, sepsis, and early death when compared with the TRISS method.
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development of a visual Analytics Tool for polytrauma patients proof of concept for a new assessment Tool using a multiple layer sankey diagram in a single center database
World Journal of Surgery, 2020Co-Authors: Ladislav Mica, Peter Bak, Avi Yaeli, Margaret Mcclain, Charles M. Lawrie, C Niggli, Hans-christoph PapeAbstract:Early physiological assessment of multiple injured patients is crucial for decision making and has relied on personal experience of trauma experts. We have developed a new visual Analytics Tool (Sankey diagram, Watson Trauma Health care Tool) that includes known prognostic parameters for polytrauma patients to help guide assessment and treatment decisions for physicians involved in trauma care. A prospectively collected trauma database of a single level I trauma center (3655 patients) was used. Inclusion criteria: age >16 years, an injury severity score (ISS) >16 and presence of a complete data set in the database. Data collected included admission values of patient age, injury scoring, shock classification, temperature, acid–base and hemostasis parameters. All of these parameters were collected daily as longitudinal parameters. Endpoints of the clinical course we considered were sepsis, SIRS and early in hospital mortality (<72 h). A proof of concept of the visualization was developed over a 2-year period in a cooperation between physicians and engineers. Statistically, the most predictive parameters were selected by binary logistic regression and ROC analysis. A dynamic interactive multilayer Sankey diagram, based on cohort similarities, was developed in a collaboration between the University Hospital of Zurich, Department of Trauma and IBM, from August 2017 until January 2018. It is a modular Tool and allows any user to add a new patient, or work with an existing case. The visualization used the data-driven documents (D3) interactive visualization library to create a responsive graphic. This application summarizes the experience of 3655 polytrauma patients and might serve as a guide for clinical decisions and educative purposes, as well as new scientific questions for the polytrauma patient. IV.
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Development of a Visual Analytics Tool for Polytrauma Patients: Proof of Concept for a New Assessment Tool Using a Multiple Layer Sankey Diagram in a Single-Center Database
World Journal of Surgery, 2019Co-Authors: Ladislav Mica, Cedric Niggli, Peter Bak, Avi Yaeli, Margaret Mcclain, Charles M. Lawrie, Hans-christoph PapeAbstract:Introduction Early physiological assessment of multiple injured patients is crucial for decision making and has relied on personal experience of trauma experts. We have developed a new visual Analytics Tool (Sankey diagram, Watson Trauma Health care Tool) that includes known prognostic parameters for polytrauma patients to help guide assessment and treatment decisions for physicians involved in trauma care. Methods A prospectively collected trauma database of a single level I trauma center (3655 patients) was used. Inclusion criteria: age >16 years, an injury severity score (ISS) >16 and presence of a complete data set in the database. Data collected included admission values of patient age, injury scoring, shock classification, temperature, acid–base and hemostasis parameters. All of these parameters were collected daily as longitudinal parameters. Endpoints of the clinical course we considered were sepsis, SIRS and early in hospital mortality (
Faruk Diblen - One of the best experts on this subject based on the ideXlab platform.
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spot open source framework for scientific data repository and interactive visualization
SoftwareX, 2019Co-Authors: Faruk Diblen, Jisk Attema, Rena Bakhshi, S Caron, Luc Hendriks, Bob StienenAbstract:Abstract spot is an open source and free visual data Analytics Tool for multi-dimensional data-sets. Its web-based interface enables user to do a quick and interactive analysis of complex data. Various operations on data are implemented such as aggregation and filtering. The interface supports OpenGL acceleration, which makes the generated charts very responsive. In order to have scalability, the software also supports PostgreSQL as its database. It follows FAIR principles to allow reuse and comparison of the published data-sets.
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spot open source framework for scientific data repository and interactive visualization
arXiv: Human-Computer Interaction, 2018Co-Authors: Faruk Diblen, Jisk Attema, Rena Bakhshi, S Caron, Luc Hendriks, Bob StienenAbstract:SPOT is an open source and free visual data Analytics Tool for multi-dimensional data-sets. Its web-based interface allows a quick analysis of complex data interactively. The operations on data such as aggregation and filtering are implemented. The generated charts are responsive and OpenGL supported. It follows FAIR principles to allow reuse and comparison of the published data-sets. The software also support PostgreSQL database for scalability.
Satoshi Nakamura - One of the best experts on this subject based on the ideXlab platform.
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a visual Analytics Tool for system logs adopting variable recommendation and feature based filtering
2013 17th International Conference on Information Visualisation, 2013Co-Authors: Aki Hayashi, Takayuki Itoh, Satoshi NakamuraAbstract:Analysis and monitoring of system logs such as transaction logs and access logs is important for various objectives including trend discovery, update effort determination, and malicious behavior monitoring. However, it is not always an easy task because these logs may be massive, consisting of millions of records containing tens of variables, and therefore it may be difficult or time-consuming to discover significant knowledge. This paper presents a visual Analytics Tool which enables us to effectively observe system logs. The Tool recommends variables that can reveal interesting discoveries and provides feature-based filtering that selects meaningful items from the visualization results. This paper also presents the result of experiments for non-professional users.
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a visual Analytics Tool for system logs adopting variable recommendation and feature based filtering
ACM Symposium on Applied Computing, 2013Co-Authors: Aki Hayashi, Takayuki Itoh, Satoshi NakamuraAbstract:Analysis and monitoring of system logs such as transaction logs and access logs is important for various objectives including trend discovery, update effort determination, and malicious behavior monitoring. However, it is not always an easy task because these logs may be massive, consisting of millions of records containing tens of variables, and therefore it may be difficult or time-consuming to discover significant knowledge. This paper presents a visual Analytics Tool which enables us to effectively observe system logs. The Tool recommends variables that can reveal interesting discoveries and provides feature-based filtering that selects meaningful items from the visualization results.
Luc Hendriks - One of the best experts on this subject based on the ideXlab platform.
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spot open source framework for scientific data repository and interactive visualization
SoftwareX, 2019Co-Authors: Faruk Diblen, Jisk Attema, Rena Bakhshi, S Caron, Luc Hendriks, Bob StienenAbstract:Abstract spot is an open source and free visual data Analytics Tool for multi-dimensional data-sets. Its web-based interface enables user to do a quick and interactive analysis of complex data. Various operations on data are implemented such as aggregation and filtering. The interface supports OpenGL acceleration, which makes the generated charts very responsive. In order to have scalability, the software also supports PostgreSQL as its database. It follows FAIR principles to allow reuse and comparison of the published data-sets.
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spot open source framework for scientific data repository and interactive visualization
arXiv: Human-Computer Interaction, 2018Co-Authors: Faruk Diblen, Jisk Attema, Rena Bakhshi, S Caron, Luc Hendriks, Bob StienenAbstract:SPOT is an open source and free visual data Analytics Tool for multi-dimensional data-sets. Its web-based interface allows a quick analysis of complex data interactively. The operations on data such as aggregation and filtering are implemented. The generated charts are responsive and OpenGL supported. It follows FAIR principles to allow reuse and comparison of the published data-sets. The software also support PostgreSQL database for scalability.