[{"publication_status":"published","citation":{"ieee":"J.-P. Herrmann <i>et al.</i>, “An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks,” <i>Procedia Computer Science</i>, vol. 200, pp. 194–204, 2022, doi: <a href=\"https://doi.org/10.1016/j.procs.2022.01.218\">https://doi.org/10.1016/j.procs.2022.01.218</a>.","apa":"Herrmann, J.-P., Tackenberg, S., Padoano, E., Hartlief, J.,  Rautenstengel, J., Loeser, C., &#38; Böhme, J. (2022). An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks. <i>Procedia Computer Science</i>, <i>200</i>, 194–204. <a href=\"https://doi.org/10.1016/j.procs.2022.01.218\">https://doi.org/10.1016/j.procs.2022.01.218</a>","chicago":"Herrmann, Jan-Phillip, Sven Tackenberg, Elio Padoano, Jörg Hartlief, Jens  Rautenstengel, Christine Loeser, and Jörg  Böhme. “An ERP Data Quality Assessment Framework for the Implementation of an APS System Using Bayesian Networks.” <i>Procedia Computer Science</i> 200 (2022): 194–204. <a href=\"https://doi.org/10.1016/j.procs.2022.01.218\">https://doi.org/10.1016/j.procs.2022.01.218</a>.","ama":"Herrmann JP, Tackenberg S, Padoano E, et al. An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks. <i>Procedia Computer Science</i>. 2022;200:194-204. doi:<a href=\"https://doi.org/10.1016/j.procs.2022.01.218\">https://doi.org/10.1016/j.procs.2022.01.218</a>","van":"Herrmann JP, Tackenberg S, Padoano E, Hartlief J,  Rautenstengel J, Loeser C, et al. An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks. Procedia Computer Science. 2022;200:194–204.","havard":"J.-P. Herrmann, S. Tackenberg, E. Padoano, J. Hartlief, J.  Rautenstengel, C. Loeser, J. Böhme, An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks, Procedia Computer Science. 200 (2022) 194–204.","bjps":"<b>Herrmann J-P <i>et al.</i></b> (2022) An ERP Data Quality Assessment Framework for the Implementation of an APS System Using Bayesian Networks. <i>Procedia Computer Science</i> <b>200</b>, 194–204.","mla":"Herrmann, Jan-Phillip, et al. “An ERP Data Quality Assessment Framework for the Implementation of an APS System Using Bayesian Networks.” <i>Procedia Computer Science</i>, vol. 200, 2022, pp. 194–204, <a href=\"https://doi.org/10.1016/j.procs.2022.01.218\">https://doi.org/10.1016/j.procs.2022.01.218</a>.","ufg":"<b>Herrmann, Jan-Phillip u. a.</b>: An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks, in: <i>Procedia Computer Science</i> 200 (2022),  S. 194–204.","short":"J.-P. Herrmann, S. Tackenberg, E. Padoano, J. Hartlief, J.  Rautenstengel, C. Loeser, J. Böhme, Procedia Computer Science 200 (2022) 194–204.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Herrmann, Jan-Phillip</span> ; <span style=\"font-variant:small-caps;\">Tackenberg, Sven</span> ; <span style=\"font-variant:small-caps;\">Padoano, Elio</span> ; <span style=\"font-variant:small-caps;\">Hartlief, Jörg</span> ; <span style=\"font-variant:small-caps;\"> Rautenstengel, Jens</span> ; <span style=\"font-variant:small-caps;\">Loeser, Christine</span> ; <span style=\"font-variant:small-caps;\">Böhme, Jörg </span>: An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks. In: <i>Procedia Computer Science</i> Bd. 200, Elsevier (2022), S. 194–204","chicago-de":"Herrmann, Jan-Phillip, Sven Tackenberg, Elio Padoano, Jörg Hartlief, Jens  Rautenstengel, Christine Loeser und Jörg  Böhme. 2022. An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks. <i>Procedia Computer Science</i> 200: 194–204. doi:<a href=\"https://doi.org/10.1016/j.procs.2022.01.218\">https://doi.org/10.1016/j.procs.2022.01.218</a>, ."},"volume":200,"title":"An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks","page":"194-204","status":"public","quality_controlled":"1","publisher":"Elsevier","department":[{"_id":"DEP7020"}],"year":"2022","author":[{"last_name":"Herrmann","first_name":"Jan-Phillip","full_name":"Herrmann, Jan-Phillip","id":"75846"},{"id":"71470","full_name":"Tackenberg, Sven","last_name":"Tackenberg","first_name":"Sven"},{"first_name":"Elio","full_name":"Padoano, Elio","last_name":"Padoano"},{"first_name":"Jörg","last_name":"Hartlief","full_name":"Hartlief, Jörg"},{"first_name":"Jens","last_name":" Rautenstengel","full_name":" Rautenstengel, Jens"},{"full_name":"Loeser, Christine","first_name":"Christine","last_name":"Loeser"},{"first_name":"Jörg ","last_name":"Böhme","full_name":"Böhme, Jörg "}],"main_file_link":[{"open_access":"1","url":"https://www.sciencedirect.com/science/article/pii/S1877050922002277"}],"abstract":[{"lang":"eng","text":"In today’s manufacturing industry, enterprise-resource-planning (ERP) systems reach their limit when planning and scheduling production subject to multiple objectives and constraints. Advanced planning and scheduling (APS) systems provide these capabilities and are an extension for ERP systems. However, when integrating an APS and ERP system, the ERP data frequently lacks quality, hindering the APS system from working as required. This paper introduces a data quality (DQ) assessment framework that employs a Bayesian Network (BN) to perform quick DQ assessments based on expert interviews and DQ measurements with actual ERP data. We explain the BN’s functionality, design, and validation and show how using the perceived DQ of experts and a semi-supervised learning algorithm improves the BN’s predictions over time. We discuss applying our framework in an APS system implementation project involving an APS system provider and a medium-sized manufacturer of hydraulic cylinders. Despite considering the DQ assessment framework in such a specific context, it is not restricted to a particular domain. We close by discussing the framework’s limits, particularly the BN as a DQ assessment methodology and future works to improve its performance."}],"date_updated":"2024-08-06T11:45:41Z","user_id":"83781","intvolume":"       200","type":"journal_article","date_created":"2023-01-25T12:00:32Z","publication_identifier":{"issn":["1877-0509 "]},"publication":"Procedia Computer Science","language":[{"iso":"eng"}],"keyword":["Data Quality Assessment","Advanced Planning","Scheduling","Bayesian Network","Enterprise Resource Planning"],"doi":"https://doi.org/10.1016/j.procs.2022.01.218","_id":"9356","oa":"1"},{"extern":"1","title":"Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting","citation":{"short":"P. Bruland, M. McGilchrist, E. Zapletal, D. Acosta, J. Proeve, S. Askin, T. Ganslandt, J. Doods, M. Dugas, BMC Medical Research Methodology 16 (2016).","din1505-2-1":"<span style=\"font-variant:small-caps;\"><span style=\"font-variant:small-caps;\">Bruland, Philipp</span> ; <span style=\"font-variant:small-caps;\">McGilchrist, Mark</span> ; <span style=\"font-variant:small-caps;\">Zapletal, Eric</span> ; <span style=\"font-variant:small-caps;\">Acosta, Dionisio</span> ; <span style=\"font-variant:small-caps;\">Proeve, Johann</span> ; <span style=\"font-variant:small-caps;\">Askin, Scott</span> ; <span style=\"font-variant:small-caps;\">Ganslandt, Thomas</span> ; <span style=\"font-variant:small-caps;\">Doods, Justin</span> ; u. a.</span>: Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. In: <i>BMC Medical Research Methodology</i> Bd. 16. London, Springer Science and Business Media LLC (2016), Nr. 1","chicago-de":"Bruland, Philipp, Mark McGilchrist, Eric Zapletal, Dionisio Acosta, Johann Proeve, Scott Askin, Thomas Ganslandt, Justin Doods und Martin Dugas. 2016. Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. <i>BMC Medical Research Methodology</i> 16, Nr. 1. doi:<a href=\"https://doi.org/10.1186/s12874-016-0259-3\">10.1186/s12874-016-0259-3</a>, .","apa":"Bruland, P., McGilchrist, M., Zapletal, E., Acosta, D., Proeve, J., Askin, S., Ganslandt, T., Doods, J., &#38; Dugas, M. (2016). Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. <i>BMC Medical Research Methodology</i>, <i>16</i>(1), Article 159. <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">https://doi.org/10.1186/s12874-016-0259-3</a>","ieee":"P. Bruland <i>et al.</i>, “Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting,” <i>BMC Medical Research Methodology</i>, vol. 16, no. 1, Art. no. 159, 2016, doi: <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">10.1186/s12874-016-0259-3</a>.","chicago":"Bruland, Philipp, Mark McGilchrist, Eric Zapletal, Dionisio Acosta, Johann Proeve, Scott Askin, Thomas Ganslandt, Justin Doods, and Martin Dugas. “Common Data Elements for Secondary Use of Electronic Health Record Data for Clinical Trial Execution and Serious Adverse Event Reporting.” <i>BMC Medical Research Methodology</i> 16, no. 1 (2016). <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">https://doi.org/10.1186/s12874-016-0259-3</a>.","van":"Bruland P, McGilchrist M, Zapletal E, Acosta D, Proeve J, Askin S, et al. Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. BMC Medical Research Methodology. 2016;16(1).","ama":"Bruland P, McGilchrist M, Zapletal E, et al. Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. <i>BMC Medical Research Methodology</i>. 2016;16(1). doi:<a href=\"https://doi.org/10.1186/s12874-016-0259-3\">10.1186/s12874-016-0259-3</a>","ufg":"<b>Bruland, Philipp u. a.</b>: Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting, in: <i>BMC Medical Research Methodology</i> 16 (2016), H. 1.","bjps":"<b>Bruland P <i>et al.</i></b> (2016) Common Data Elements for Secondary Use of Electronic Health Record Data for Clinical Trial Execution and Serious Adverse Event Reporting. <i>BMC Medical Research Methodology</i> <b>16</b>.","havard":"P. Bruland, M. McGilchrist, E. Zapletal, D. Acosta, J. Proeve, S. Askin, T. Ganslandt, J. Doods, M. Dugas, Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting, BMC Medical Research Methodology. 16 (2016).","mla":"Bruland, Philipp, et al. “Common Data Elements for Secondary Use of Electronic Health Record Data for Clinical Trial Execution and Serious Adverse Event Reporting.” <i>BMC Medical Research Methodology</i>, vol. 16, no. 1, 159, 2016, <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">https://doi.org/10.1186/s12874-016-0259-3</a>."},"volume":16,"author":[{"orcid":"0000-0001-6939-7630","last_name":"Bruland","id":"75847","full_name":"Bruland, Philipp","first_name":"Philipp"},{"last_name":"McGilchrist","full_name":"McGilchrist, Mark","first_name":"Mark"},{"full_name":"Zapletal, Eric","first_name":"Eric","last_name":"Zapletal"},{"full_name":"Acosta, Dionisio","last_name":"Acosta","first_name":"Dionisio"},{"full_name":"Proeve, Johann","first_name":"Johann","last_name":"Proeve"},{"first_name":"Scott","full_name":"Askin, Scott","last_name":"Askin"},{"full_name":"Ganslandt, Thomas","last_name":"Ganslandt","first_name":"Thomas"},{"first_name":"Justin","last_name":"Doods","full_name":"Doods, Justin"},{"last_name":"Dugas","first_name":"Martin","full_name":"Dugas, Martin"}],"year":"2016","status":"public","date_created":"2024-07-18T13:31:36Z","publication_identifier":{"issn":["1471-2288"]},"publication":"BMC Medical Research Methodology","date_updated":"2024-07-18T13:33:42Z","language":[{"iso":"eng"}],"keyword":["Clinical trials","Common data elements","Data quality","Electronic health records","Metadata","Secondary use"],"publication_status":"published","department":[{"_id":"DEP5024"}],"issue":"1","abstract":[{"lang":"eng","text":"Background: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.\r\n\r\nMethods: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project.\r\n\r\nResults: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records.\r\n\r\nConclusions: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list."}],"publisher":"Springer Science and Business Media LLC","type":"scientific_journal_article","user_id":"83781","intvolume":"        16","place":"London","article_number":"159","doi":"10.1186/s12874-016-0259-3","_id":"11745"}]