Tofangchi, Schahin

[["dc.bibliographiccitation.firstpage","246"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Management Information Systems"],["dc.bibliographiccitation.lastpage","276"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Tofangchi, Schahin"],["dc.contributor.author","Hanelt, André"],["dc.contributor.author","Marz, David"],["dc.contributor.author","Kolbe, Lutz M."],["dc.date.accessioned","2021-06-01T09:41:50Z"],["dc.date.available","2021-06-01T09:41:50Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1080/07421222.2021.1870391"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85055"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.eissn","1557-928X"],["dc.relation.issn","0742-1222"],["dc.title","Handling the Efficiency–Personalization Trade-Off in Service Robotics: A Machine-Learning Approach"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","145"],["dc.bibliographiccitation.lastpage","159"],["dc.contributor.author","Tofangchi, Schahin"],["dc.contributor.author","Hanelt, Andre"],["dc.contributor.author","Kolbe, Lutz M."],["dc.date.accessioned","2018-08-26T14:50:09Z"],["dc.date.available","2018-08-26T14:50:09Z"],["dc.date.issued","2017"],["dc.description.abstract","The process of Datafication gives rise to ubiquitousness of data. Data-driven approaches may create meaningful insights from the vast volumes of data available to businesses. However, coping with the great volume and variety of data requires improved data analysis methods. Many such methods are dependent on a user’s subjective domain knowledge. This dependency leads to a barrier for the use of sophisticated statistical methods, because a user would have to invest a significant amount of labor into the customization of such methods in order to incorporate domain knowledge into them. We argue that machines may efficiently support researchers and analysts even with non-quantitative data once they are equipped with the ability to develop their own subjective domain knowledge in a way that the amount of manual customization is reduced. Our contribution is a design theory – called the Divisionof-Labor Framework – for generating and using Experts that can develop domain knowledge."],["dc.identifier.doi","10.1007/978-3-319-59144-5_9"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15551"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.conference","Twelfth International Conference on Design Science Research in Information Systems and Technology"],["dc.relation.eventend","2017-06-01"],["dc.relation.eventlocation","Germany"],["dc.relation.eventstart","2017-05-30"],["dc.relation.ispartof","Lecture Notes in Computer Science"],["dc.relation.issn","0302-9743"],["dc.title","Towards Distributed Cognitive Expert Systems"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Tofangchi, Schahin"],["dc.contributor.author","Hanelt, Andre"],["dc.contributor.author","Böhrnsen, Florian"],["dc.date.accessioned","2018-06-26T08:37:40Z"],["dc.date.available","2018-06-26T08:37:40Z"],["dc.date.issued","2018"],["dc.description.abstract","Although researchers have uncovered potential positive impacts of digital technologies in healthcare and medical centers have been increasingly making use of technology to digitally store their data, the use of healthcare analytics in clinical practice remains limited. In particular, the application of machine learning (ML) approaches, although holding the potential of providing valuable insights, is mainly restricted to descriptive ML, due to the approximate nature of ML, the impact of inaccuracies, and the perceived potential additional efforts in clinical workflows. Taking into account these barriers to healthcare analytics adoption, in this multidisciplinary study, we obtained and jointly analyzed cancer data on 799 cases of cranio-maxillofacial and oral-maxillofacial surgery. We developed a real-time decision support system that predicts optimal treatments and communicates its prediction confidence along with patient attributes that are significant to decision making, thereby providing potentials simultaneously for improving quality of care and for increasing process efficiency for physicians."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15151"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.notes.status","zu prüfen"],["dc.publisher","AIS Electronic Library"],["dc.relation.conference","ICIS 2017: Transforming Society with Digital Innovation"],["dc.relation.eventend","2017-12-13"],["dc.relation.eventlocation","Seoul"],["dc.relation.eventstart","2017-12-09"],["dc.relation.iserratumof","yes"],["dc.title","Distributed Cognitive Expert Systems in Cancer Data Analytics: A Decision Support System for Oral and Maxillofacial Surgery"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2176"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Healthcare"],["dc.bibliographiccitation.volume","10"],["dc.contributor.affiliation","Klumpp, Matthias; 1Fraunhofer IML Dortmund, 44227 Dortmund, Germany"],["dc.contributor.affiliation","Hanelt, André; 3Faculty of Economics and Management, University of Kassel, 34125 Kassel, Germany"],["dc.contributor.affiliation","Greve, Maike; 2Department of Business Administration, University of Göttingen, 37073 Göttingen, Germany"],["dc.contributor.affiliation","Kolbe, Lutz M.; 2Department of Business Administration, University of Göttingen, 37073 Göttingen, Germany"],["dc.contributor.affiliation","Tofangchi, Schahin; 4Soramitsu Co., Ltd., Tokyo 151-0051, Japan"],["dc.contributor.affiliation","Böhrnsen, Florian; 5University Medical Center Göttingen, 37075 Göttingen, Germany"],["dc.contributor.affiliation","Jakob, Jens; 5University Medical Center Göttingen, 37075 Göttingen, Germany"],["dc.contributor.affiliation","Kaczmarek, Sylvia; 1Fraunhofer IML Dortmund, 44227 Dortmund, Germany"],["dc.contributor.affiliation","Börsting, Ingo; 6Faculty of Business Administration and Economics, University of Duisburg-Essen, 45127 Essen, Germany"],["dc.contributor.affiliation","Ehmke, Christopher; 6Faculty of Business Administration and Economics, University of Duisburg-Essen, 45127 Essen, Germany"],["dc.contributor.affiliation","Düsing, Helena; 7University Medical Center of Münster, 48149 Münster, Germany"],["dc.contributor.affiliation","Juhra, Christian; 7University Medical Center of Münster, 48149 Münster, Germany"],["dc.contributor.author","Klumpp, Matthias"],["dc.contributor.author","Hanelt, André"],["dc.contributor.author","Greve, Maike"],["dc.contributor.author","Kolbe, Lutz M."],["dc.contributor.author","Tofangchi, Schahin"],["dc.contributor.author","Böhrnsen, Florian"],["dc.contributor.author","Jakob, Jens"],["dc.contributor.author","Kaczmarek, Sylvia"],["dc.contributor.author","Börsting, Ingo"],["dc.contributor.author","Ehmke, Christopher"],["dc.contributor.author","Juhra, Christian"],["dc.contributor.author","Düsing, Helena"],["dc.contributor.editor","Carreras, Joaquim"],["dc.date.accessioned","2022-12-01T08:31:40Z"],["dc.date.available","2022-12-01T08:31:40Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-28T11:15:20Z"],["dc.description.abstract","Digital applications in health care are a concurrent research and management question, where implementation experiences are a core field of information systems research. It also contributes to fighting pandemic crises like COVID-19 because contactless information flow and speed of diagnostics are improved. This paper presents three digital application case studies from emergency medicine, administration management, and cancer diagnosis with AI support from the University Medical Centers of Münster and Göttingen in Germany. All cases highlight the potential of digitalization to increase speed and efficiency within the front end of medicine as the crucial phase before patient treatment starts. General challenges for health care project implementations and human-computer interaction (HCI) concepts in health care are derived and discussed, including the importance of specific processes together with user analysis and adaption. A derived concept for HCI includes the criteria speed, accuracy, modularity, and individuality to achieve sustainable improvements within the front end of medicine."],["dc.description.sponsorship","central publication fund"],["dc.identifier.doi","10.3390/healthcare10112176"],["dc.identifier.pii","healthcare10112176"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118231"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.publisher","MDPI"],["dc.relation.eissn","2227-9032"],["dc.rights","Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)."],["dc.title","Accelerating the Front End of Medicine: Three Digital Use Cases and HCI Implications"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]