Ein generischer Algorithmus zur Erkennung temporaler Koinzidenzen zwischen Medikamentenapplikationen und Laborwertveränderungen

Background and Aims: One of the hazards in the use of drugs is the ever-present possibility of harmful effects. However, the assessment of a possible causal relationship between the intake of a drug and its effect is a complex process and requires the fulfillment of several criteria. One of these criteria is the presence of a temporal relationship between the intake of a drug and an observed effect such as the alteration of a laboratory value curve. The aim of this thesis was to develop an algorithm that detects this kind of coincidences within data that have not been collected for that purpose in the first place – such as data from electronic health records. Methods: Prior to the development of the actual computational algorithm, a set of data has been extracted from the data warehouse of the University Hospital of Erlangen. This set of data has then been annotated by human experts who had analyzed the data for the presence of temporal relationships between the intake of a drug and the alteration of a laboratory value curve. The resulting Ground Truth has then been broken down and the decision rules for the algorithm have been induced by means of feature engineering. The validation has been performed deductively by applying the algorithm to both the Ground Truth data corpus and a control dataset. Results: The major result – beside the 400 data episodes of the Ground Truth data corpus – was an algorithm which is capable of detecting possible coincidences between the intake of a drug and the alteration of a laboratory value with a specificity of 0.932. Furthermore, the algorithm is capable of detecting non-existing coincidences with a sensitivity of 0.967. A reference implementation in the Python programming language is available. Conclusions: The aim has been completely achieved. Although there are no results of a practical application, the high specificity for the detection of non-existing coincidences and the high sensitivity for the detection of existing coincidences qualify the algorithm for its usage in further research in the field of pharmacovigilance.Hintergrund und Ziele: Eine der Gefahren bei der Anwendung von Medikamenten ist die stets vorhandene Möglichkeit schädigender Wirkungen – die Beurteilung eines möglichen kausalen Zusammenhangs zwischen einer Medikamenteneinnahme und einer Wirkung ist jedoch ein komplexer Prozess und erfordert die Erfüllung mehrerer Kriterien. Eines dieser Kriterien ist das Vorliegen eines zeitlichen Zusammenhangs zwischen einer Medikamenteneinnahme und einer beobachteten Wirkung, wie beispielsweise der Veränderung einer Laborwertkurve. Ziel der vorliegenden Arbeit war es, einen Algorithmus zu entwickeln, der solche Koinzidenzen innerhalb von Datenbeständen erkennt, die ursprünglich nicht für diesen Zweck gesammelt wurden, wie z.B. Daten aus elektronischen Patientenakten. Methoden: Vor der Entwicklung des eigentlichen computergestützten Algorithmus‘ wurde zunächst eine Datenbasis aus dem Data Warehouse des Universitätsklinikums Erlangen extrahiert, von menschlichen Experten auf das Vorliegen zeitlicher Zusammenhänge zwischen einer Medikamenteneinnahme und der Veränderung einer Laborwertkurve untersucht und entsprechend annotiert. Die so ermittelte Ground Truth wurde anschließend analysiert und mittels empirischer Merkmalsextraktion wurden die für den Algorithmus nötigen Entscheidungsregeln induziert. Die Validierung erfolge deduktiv durch Anwendung des Algorithmus‘ auf die Ground Truth und einen Kontrolldatensatz. Ergebnisse: Neben dem aus 400 Datenepisoden bestehenden Ground-Truth-Datenkorpus entstand als Hauptergebnis ein Algorithmus, der das Vorliegen einer möglichen Koinzidenz zwischen einer Medikamentengabe und einer Laborwertveränderung mit einer Spezifität von 0,932 und das Nichtvorliegen einer solchen mit einer Sensitivität von 0,967 bestimmen kann. Eine Referenzimplementierung des Algorithmus’ in der Programmiersprache Python liegt vor. Schlussfolgerungen: Das gesetzte Ziel wurde vollumfänglich erreicht. Obwohl es keine Ergebnisse aus einer praktischen Anwendung gibt, qualifizieren die hohe Spezifität für die Erkennung von nicht vorhandenen Koinzidenzen und die hohe Sensitivität für die Erkennung von möglicherweise vorhandenen Koinzidenzen den Algorithmus zum Einsatz für weitere Forschung auf dem Gebiet der Pharmakovigilanz

Applied Practice and Possible Leverage Points for Information Technology Support for Patient Screening in Clinical Trials: Qualitative Study

Background: Clinical trials are one of the most challenging and meaningful designs in medical research. One essential step before starting a clinical trial is screening, that is, to identify patients who fulfill the inclusion criteria and do not fulfill the exclusion criteria. The screening step for clinical trials might be supported by modern information technology (IT). Objective: This explorative study aimed (1) to obtain insights into which tools for feasibility estimations and patient screening are actually used in clinical routine and (2) to determine which method and type of IT support could benefit clinical staff. Methods: Semistandardized interviews were conducted in 5 wards (cardiology, gynecology, gastroenterology, nephrology, and palliative care) in a German university hospital. Of the 5 interviewees, 4 were directly involved in patient screening. Three of them were clinicians, 1 was a study nurse, and 1 was a research assistant. Results: The existing state of study feasibility estimation and the screening procedure were dominated by human communication and estimations from memory, although there were many possibilities for IT support. Success mostly depended on the experience and personal motivation of the clinical staff. Electronic support has been used but with little importance so far. Searches in ward-specific patient registers (databases) and searches in clinical information systems were reported. Furthermore, free-text searches in medical reports were mentioned. For potential future applications, a preference for either proactive or passive systems was not expressed. Most of the interviewees saw the potential for the improvement of the actual systems, but they were also largely satisfied with the outcomes of the current approach. Most of the interviewees were interested in learning more about the various ways in which IT could support and relieve them in their clinical routine. Conclusions: Overall, IT support currently plays a minor role in the screening step for clinical trials. The lack of IT usage and the estimations made from memory reported by all the participants might constrain cognitive resources, which might distract from clinical routine. We conclude that electronic support for the screening step for clinical trials is still a challenge and that education of the staff about the possibilities for electronic support in clinical trials is necessary

MeVisLab 3.0.x Community PDF-AddOn (2017-09-28 release)

These installers are self-contained, executable archives that automatically add all files of the MeVisLab Community PDF-Addon to an existing MeVisLab installation. This includes the source code as well as binary files. The target groups for these installers are MeVisLab newcomers and pure users that want to use the PDF-Addon features out-of-the-box. Future updates will be made available via Zenodo as well. This release contains installers for the follwing MeVisLab versions and operating systems: MeVisLab 3.0.x / Windows Visual Studio 2017 X64 MeVisLab 3.0.x / Windows Visual Studio 2015 X64 MeVisLab 3.0.x / Windows Visual Studio 2015 MeVisLab 3.0.x / Windows Visual Studio 2013 X64 MeVisLab 3.0.x / Linux 64Bit (tested with Ubuntu 14.04.2) MeVisLab 3.0.x / Mac OS X Intel 64Bit Important note: Error messages reagarding an incorrect MeVisLab version or a missing MeVisLab installation can be ignored if MeVisLab 3.0 or a sub-version (3.0.1 etc.) is installed. (The installers have been built with the latest MeVisLab version which might not be available for the public.) If the MeVisLab installation path could not be detected automatically, you must select it manually. In this case, select the path which contains the "Packages" folder as installation path. This is usually the standard installation path for applications + "MeVisLab" + Version Number (e.g., for Windows: C:\Program Files\MeVisLab3.0.1VC14-64). Installers for MeVisLab 2.8 are available from http://dx.doi.org/10.5281/zenodo.155381 Installers for MeVisLab 2.7 are available from http://dx.doi.org/10.5281/zenodo.47491 All those who are interested in being able to always use the latest version should connect their MeVisLab installation with the community sources which are hosted at GitHub [URL: https://github.com/MeVisLab/communitymodules/tree/master/Community; clone URL: https://github.com/MeVisLab/communitymodules.git]. This approach, however, requires compiling the source code and is intended only for experienced users or for users that are willing to become acquainted with MeVisLab. If you use this work for publications, please cite the following articles: Newe A. (2016) Enriching scientific publications with interactive 3D PDF: an integrated toolbox for creating ready-to-publish figures. PeerJ Computer Science 2:e64 https://doi.org/10.7717/peerj-cs.64 Newe A. (2015) Towards an easier creation of three-dimensional data for embedding into scholarly 3D PDF (Portable Document Format) files. PeerJ 3:e794 https://dx.doi.org/10.7717/peerj.794. PMID: 25780759

MeVisLab Community PDF-AddOn (2016-09-12 release)

These installers are self-contained, executable archives that automatically add all files of the MeVisLab Community PDF-Addon to an existing MeVisLab installation. This includes the source code as well as binary files. The target groups for these installers are MeVisLab newcomers and pure users that want to use the PDF-Addon features out-of-the-box. Future updates will be made available via Zenodo as well. This release contains installers for the follwing MeVisLab versions and operating systems: MeVisLab 2.8.x / Windows Visual Studio 2015 X64 MeVisLab 2.8.x / Windows Visual Studio 2013 X64 MeVisLab 2.8.x / Windows Visual Studio 2010 X64 MeVisLab 2.8.x / Linux 64Bit (tested with Ubuntu 14.04.2) MeVisLab 2.8.x / Mac OS X Intel 64Bit Important note: Error messages reagarding an incorrect MeVisLab version or a missing MeVisLab installation can be ignored if MeVisLab 2.8 or a sub-version (2.8.1 etc.) is installed. (The installers have been built with the latest MeVisLab version which might not be available for the public.) If the MeVisLab installation path could not be detected automatically, you must select it manually. In this case, select the path which contains the "Packages" folder as installation path. This is usually the standard installation path for applications + "MeVisLab" + Version Number (e.g., for Windows: C:\Program Files\MeVisLab2.8.2VC14-64). Installers for MeVisLab 3.0.x are available from http://dx.doi.org/10.5281/zenodo.998690 Installers for MeVisLab 2.7 are available from http://dx.doi.org/10.5281/zenodo.47491 All those who are interested in being able to always use the latest version should connect their MeVisLab installation with the community sources which are hosted at GitHub [URL: https://github.com/MeVisLab/communitymodules/tree/master/Community; clone URL: https://github.com/MeVisLab/communitymodules.git]. This approach, however, requires compiling the source code and is intended only for experienced users or for users that are willing to become acquainted with MeVisLab. If you use this work for publications, please cite the following articles: Newe A. (2016) Enriching scientific publications with interactive 3D PDF: an integrated toolbox for creating ready-to-publish figures. PeerJ Computer Science 2:e64 https://doi.org/10.7717/peerj-cs.64 Newe A. (2015) Towards an easier creation of three-dimensional data for embedding into scholarly 3D PDF (Portable Document Format) files. PeerJ 3:e794 https://dx.doi.org/10.7717/peerj.794. PMID: 25780759

Dramatyping: A generic algorithm for detecting reasonable temporal correlations between drug administration and lab value alterations

Abstract According to the World Health Organization, one of the criteria for the standardized assessment of case causality in adverse drug reactions is the temporal relationship between the intake of a drug and the occurrence of a reaction or a laboratory test abnormality. This article presents and describes an algorithm for the detection of a reasonable temporal correlation between the administration of a drug and the alteration of a laboratory value course. The algorithm is designed to process normalized lab values and is therefore universally applicable. It has a sensitivity of 0.932 for the detection of lab value courses that show changes in temporal correlation with the administration of a drug and it has a specificity of 0.967 for the detection of lab value courses that show no changes. Therefore the algorithm is appropriate to screen the data of electronic health records and to support human experts in revealing adverse drug reactions. A reference implementation in Python programming language is available

Dramatyping: a generic algorithm for detecting reasonable temporal correlations between drug administration and lab value alterations

According to the World Health Organization, one of the criteria for the standardized assessment of case causality in adverse drug reactions is the temporal relationship between the intake of a drug and the occurrence of a reaction or a laboratory test abnormality. This article presents and describes an algorithm for the detection of a reasonable temporal correlation between the administration of a drug and the alteration of a laboratory value course. The algorithm is designed to process normalized lab values and is therefore universally applicable. It has a sensitivity of 0.932 for the detection of lab value courses that show changes in temporal correlation with the administration of a drug and it has a specificity of 0.967 for the detection of lab value courses that show no changes. Therefore, the algorithm is appropriate to screen the data of electronic health records and to support human experts in revealing adverse drug reactions. A reference implementation in Python programming language is available

Towards an easier creation of three-dimensional data for embedding into scholarly 3D PDF (Portable Document Format) files

The Portable Document Format (PDF) allows for embedding three-dimensional (3D) models and is therefore particularly suitable to communicate respective data, especially as regards scholarly articles. The generation of the necessary model data, however, is still challenging, especially for inexperienced users. This prevents an unrestrained proliferation of 3D PDF usage in scholarly communication. This article introduces a new solution for the creation of three of types of 3D geometry (point clouds, polylines and triangle meshes), that is based on MeVisLab, a framework for biomedical image processing. This solution enables even novice users to generate the model data files without requiring programming skills and without the need for an intensive training by simply using it as a conversion tool. Advanced users can benefit from the full capability of MeVisLab to generate and export the model data as part of an overall processing chain. Although MeVisLab is primarily designed for handling biomedical image data, the new module is not restricted to this domain. It can be used for all scientific disciplines

MeVisLab Community PDF-AddOn (2016-03-30 release) - DEPRECATED

<p><strong>ATTENTION!</strong></p> <p><strong>This version is deprecated. Please use the following resources for a newer version: </strong></p> <p><strong>http://dx.doi.org/10.5281/zenodo.</strong><strong>155381</strong> <strong>for MeVisLab 2.8(.x)</strong></p> <p><strong>http://dx.doi.org/10.5281/zenodo.47491 for MeVisLab 2.7(.x)</strong><br>  </p> <p> </p> <p>_____________________________________________________________________</p> <p> </p> <p>These installers are self-contained, executable archives that automatically add all files of the MeVisLab Community PDF-Addon to an existing MeVisLab installation. This includes the source code as well as binary files.</p> <p>The target groups for these installers are MeVisLab newcomers and pure users that want to use the PDF-Addon features out-of-the-box. Future updates will be made available via Zenodo as well.</p> <p>This release contains installers for the follwing MeVisLab versions and operating systems:</p> <ul> <li>MeVisLab 2.8 / Windows Visual Studio 2015 X64</li> <li>MeVisLab 2.8 / Windows Visual Studio 2013 X64</li> <li>MeVisLab 2.8 / Windows Visual Studio 2010 X64</li> <li>MeVisLab 2.8 / Linux 64Bit (tested with Ubuntu 14.04.2)</li> <li>MeVisLab 2.8 / Mac OS X Intel 64Bit</li> </ul> <p>All those who are interested in being able to always use the latest version should connect their MeVisLab installation with the community sources which are hosted at GitHub [URL: https://github.com/MeVisLab/communitymodules/tree/master/Community; clone URL: https://github.com/MeVisLab/communitymodules.git]. This approach, however, requires compiling the source code and is intended only for experienced users or for users that are willing to become acquainted with MeVisLab.</p> <p>If you use this work for publications, please cite the following article:</p> <p>Newe A. (2015) <em>Towards an easier creation of three-dimensional data for embedding into scholarly 3D PDF (Portable Document Format) files</em>. PeerJ 3:e794 https://dx.doi.org/10.7717/peerj.794. PMID: 25780759.</p> <p>Another article that describes this add-on in its entirety is currently under peer-review. A preprint can be downloaded here:</p> <p>Newe A. (2016) <em>Enriching scientific publications with interactive 3D PDF figures: A complete toolbox</em>. PeerJ PrePrints 4:e1594v2 https://doi.org/10.7287/peerj.preprints.1594v2</p

MeVisLab Community PDF-AddOn (2016-01-26 release) - DEPRECATED

<p><strong>ATTENTION!</strong></p> <p><strong>This version is deprecated. Please use the following resources for a newer version: </strong></p> <p><strong>http://dx.doi.org/10.5281/zenodo.</strong><strong>155381</strong> <strong>for MeVisLab 2.8(.x)</strong></p> <p><strong>http://dx.doi.org/10.5281/zenodo.47491 for MeVisLab 2.7(.x)</strong></p> <p> </p> <p>_____________________________________________________________________</p> <p> </p> <p>These installers are self-contained, executable archives that automatically add all files of the MeVisLab Community PDF-Addon to an existing MeVisLab installation. This includes the source code as well as binary files.</p> <p>The target groups for these installers are MeVisLab newcomers and pure users that want to use the PDF-Addon features out-of-the-box. Future updates will be made available via Zenodo as well.</p> <p>This release contains installers for the follwing MeVisLab versions and operating systems:</p> <ul> <li>MeVisLab 2.7(.x) / Windows Visual Studio 2013 X64</li> <li>MeVisLab 2.7(.x) / Linux 64Bit (tested with Ubuntu 14.04.2)</li> <li>MeVisLab 2.7(.x) / Mac OS X Intel 64Bit</li> </ul> <p><strong>Important note:</strong> Error messages reagarding an incorrect MeVisLab version or a missing MeVisLab installation can be ignored if MeVisLab 2.7 or a sub-version (2.7.1 etc.) is installed. The installers have been built with the latest MeVisLab version which might not be available to the public.</p> <p>All those who are interested in being able to always use the latest version should connect their MeVisLab installation with the community sources which are hosted at GitHub [URL: https://github.com/MeVisLab/communitymodules/tree/master/Community; clone URL: https://github.com/MeVisLab/communitymodules.git]. This approach, however, requires compiling the source code and is intended only for experienced users or for users that are willing to become acquainted with MeVisLab.</p> <p>If you use this work for publications, please cite the following article:</p> <p>Newe A. (2015) <em>Towards an easier creation of three-dimensional data for embedding into scholarly 3D PDF (Portable Document Format) files</em>. PeerJ 3:e794 https://dx.doi.org/10.7717/peerj.794. PMID: 25780759.</p

MeVisLab Community PDF-AddOn (2016-03-14 release)

<p>These installers are self-contained, executable archives that automatically add all files of the MeVisLab Community PDF-Addon to an existing MeVisLab installation. This includes the source code as well as binary files.</p> <p>The target groups for these installers are MeVisLab newcomers and pure users that want to use the PDF-Addon features out-of-the-box. Future updates will be made available via Zenodo as well.</p> <p>This release contains installers for the follwing MeVisLab versions and operating systems:</p> <ul> <li>MeVisLab 2.7(.x) / Windows Visual Studio 2013 X64</li> <li>MeVisLab 2.7(.x) / Linux 64Bit (tested with Ubuntu 14.04.2)</li> <li>MeVisLab 2.7(.x) / Mac OS X Intel 64Bit</li> </ul> <p><strong>Important note:</strong> Error messages reagarding an incorrect MeVisLab version or a missing MeVisLab installation can be ignored if MeVisLab 2.7 or a sub-version (2.7.1 etc.) is installed. The installers have been built with the latest MeVisLab version which might not be available to the public.</p> <p><strong>Installers for MeVisLab 2.8(.x) are available at http://dx.doi.org/10.5281/zenodo.155381</strong></p> <p>All those who are interested in being able to always use the latest version should connect their MeVisLab installation with the community sources which are hosted at GitHub [URL: https://github.com/MeVisLab/communitymodules/tree/master/Community; clone URL: https://github.com/MeVisLab/communitymodules.git]. This approach, however, requires compiling the source code and is intended only for experienced users or for users that are willing to become acquainted with MeVisLab.</p> <p>If you use this work for publications, please cite the following articles:</p> <p>Newe A. (2016) <em>Enriching scientific publications with interactive 3D PDF: an integrated toolbox for creating ready-to-publish figures</em>. PeerJ Computer Science 2:e64 https://doi.org/10.7717/peerj-cs.64</p> <p>Newe A. (2015) <em>Towards an easier creation of three-dimensional data for embedding into scholarly 3D PDF (Portable Document Format) files</em>. PeerJ 3:e794 https://dx.doi.org/10.7717/peerj.794. PMID: 25780759.</p