TY - JOUR A1 - Grum, Marcus A1 - Bender, Benedict A1 - Alfa, A. S. A1 - Gronau, Norbert T1 - A decision maxim for efficient task realization within analytical network infrastructures JF - Decision support systems : DSS ; the international journal N2 - 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. KW - Analytics KW - Architecture concepts KW - Cyber-physical systems KW - Internet of things KW - Task realization strategies KW - Simulation Y1 - 2018 U6 - https://doi.org/10.1016/j.dss.2018.06.005 SN - 0167-9236 SN - 1873-5797 VL - 112 SP - 48 EP - 59 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Grum, Marcus T1 - Manufacturing Analytics JF - Von Industrial Internet of Things zu Industrie 4.0. Band 2 Y1 - 2018 SN - 978-3-95545-261-2 SP - 149 EP - 190 PB - Gito CY - Berlin ER - TY - JOUR A1 - Grum, Marcus A1 - Sultanow, Eldar A1 - Friedmann, Daniel A1 - Ulrich, Andre A1 - Gronau, Norbert T1 - Tools des Maschinellen Lernens BT - Marktstudie, Anwendungsbereiche & Lösungen der Künstlichen Intelligenz N2 - Künstliche Intelligenz ist in aller Munde. Immer mehr Anwendungsbereiche werden durch die Auswertung von vorliegenden Daten mit Algorithmen und Frameworks z.B. des Maschinellen Lernens erschlossen. Dieses Buch hat das Ziel, einen Überblick über gegenwärtig vorhandene Lösungen zu geben und darüber hinaus konkrete Hilfestellung bei der Auswahl von Algorithmen oder Tools bei spezifischen Problemstellungen zu bieten. Um diesem Anspruch gerecht zu werden, wurden 90 Lösungen mittels einer systematischen Literaturrecherche und Praxissuche identifiziert sowie anschließend klassifiziert. Mit Hilfe dieses Buches gelingt es, schnell die notwendigen Grundlagen zu verstehen, gängige Anwendungsgebiete zu identifizieren und den Prozess zur Auswahl eines passenden ML-Tools für das eigene Projekt systematisch zu meistern. Y1 - 2021 SN - 978-3-95545-380-0 SN - 978-3-95545-318-7 U6 - https://doi.org/10.30844/grum_2020 PB - Gito CY - Berlin ER - TY - JOUR A1 - Ploner, Tina A1 - Hess, Steffen A1 - Grum, Marcus A1 - Drewe-Boss, Philipp A1 - Walker, Jochen T1 - Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease JF - Statistical methods in medical research N2 - 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. KW - Gradient boosting KW - stability selection KW - claims data KW - disease trajectory KW - chronic obstructive pulmonary disease Y1 - 2020 U6 - https://doi.org/10.1177/0962280220938088 SN - 0962-2802 SN - 1477-0334 VL - 29 IS - 12 SP - 3684 EP - 3694 PB - Sage Publ. CY - London [u.a.] ER -