@article{PlonerHessGrumetal.2020,
  author    = {Ploner, Tina and Hess, Steffen and Grum, Marcus and Drewe-Boss, Philipp and Walker, Jochen},
  title     = {Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease},
  series = {Statistical methods in medical research},
  volume    = {29},
  journal   = {Statistical methods in medical research},
  number    = {12},
  publisher = {Sage Publ.},
  address   = {London [u.a.]},
  issn      = {0962-2802},
  doi       = {10.1177/0962280220938088},
  pages     = {3684 -- 3694},
  year      = {2020},
  abstract  = {Objective We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection. Materials and methods We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity. We then used gradient boosting with stability selection to determine variables correlating with a chronic obstructive pulmonary disease diagnosis of highest severity and subsequently model the temporal progression of the disease using the selected variables. Results We identified a network of 20 diagnoses (e.g. respiratory failure), medications (e.g. anticholinergic drugs) and procedures associated with a subsequent chronic obstructive pulmonary disease diagnosis of highest severity. Furthermore, the network successfully captured temporal patterns, such as disease progressions from lower to higher severity grades. Discussion The temporal trajectories identified by our data-driven approach are compatible with existing knowledge about chronic obstructive pulmonary disease showing that the method can reliably select relevant variables in a high-dimensional context. Conclusion We provide a generalizable approach for the automatic detection of disease trajectories in claims data. This could help to diagnose diseases early, identify unknown risk factors and optimize treatment plans.},
  language  = {en}
}
@article{KlippertStolpmannGrumetal.2023,
  author    = {Klippert, Monika and Stolpmann, Robert and Grum, Marcus and Thim, Christof and Gronau, Norbert and Albers, Albert},
  title     = {Knowledge transfer quality improvement},
  series = {Procedia CIRP},
  volume    = {119},
  journal   = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2023.02.171},
  pages     = {919 -- 925},
  year      = {2023},
  abstract  = {Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect.},
  language  = {en}
}
@article{GrumHiesslMareschetal.2021,
  author    = {Grum, Marcus and Hiessl, Werner and Maresch, Karl and Gronau, Norbert},
  title     = {Design of a neuronal training modeling language},
  series = {AIS-Transactions on enterprise systems},
  volume    = {5},
  journal   = {AIS-Transactions on enterprise systems},
  number    = {1},
  publisher = {GITO-Publ., Verl. f{\"u}r Industrielle Informationstechnik und Organisation},
  address   = {Berlin},
  issn      = {1867-7134},
  doi       = {10.30844/aistes.v5i1.20},
  pages     = {16},
  year      = {2021},
  abstract  = {As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.},
  language  = {en}
}
@article{GrumBenderAlfaetal.2018,
  author    = {Grum, Marcus and Bender, Benedict and Alfa, A. S. and Gronau, Norbert},
  title     = {A decision maxim for efficient task realization within analytical network infrastructures},
  series = {Decision support systems : DSS ; the international journal},
  volume    = {112},
  journal   = {Decision support systems : DSS ; the international journal},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-9236},
  doi       = {10.1016/j.dss.2018.06.005},
  pages     = {48 -- 59},
  year      = {2018},
  abstract  = {Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation.},
  language  = {en}
}