TY  - JOUR
A1  - Werfel, Stanislas
A1  - Günthner, Roman
A1  - Hapfelmeier, Alexander
A1  - Hanssen, Henner
A1  - Kotliar, Konstantin
A1  - Heemann, Uwe
A1  - Schmaderer, Christoph
ED  - Guzik, Tomasz J.
T1  - Identification of cardiovascular high-risk groups from dynamic retinal vessel signals using untargeted machine learning
JF  - Cardiovascular Research
N2  - Dynamic retinal vessel analysis (DVA) provides a non-invasive way to assess microvascular function in patients and potentially to improve predictions of individual cardiovascular (CV) risk. The aim of our study was to use untargeted machine learning on DVA in order to improve CV mortality prediction and identify corresponding response alterations.
KW  - Machine learning
KW  - Retinal vessels
KW  - Microcirculation
KW  - Haemodialysis
KW  - Myocardial infarction and cardiac death
Y1  - 2022
U6  - http://dx.doi.org/10.1093/cvr/cvab040
SN  - 0008-6363
VL  - 118
IS  - 2
SP  - 612
EP  - 621
PB  - Oxford University Press
CY  - Oxford
ER  - 
TY  - CHAP
A1  - Stenger, David
A1  - Altherr, Lena
A1  - Abel, Dirk
T1  - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead
T2  - Operations Research Proceedings 2018
N2  - In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.
KW  - Product family optimization
KW  - Mixed-integer nonlinear black-box optimization
KW  - Engineering optimization
KW  - Machine learning
Y1  - 2019
SN  - 978-3-030-18499-5  (Print)
SN  - 978-3-030-18500-8 (Online)
U6  - http://dx.doi.org/10.1007/978-3-030-18500-8_47
SP  - 379
EP  - 385
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Kohl, Philipp
A1  - Schmidts, Oliver
A1  - Klöser, Lars
A1  - Werth, Henri
A1  - Kraft, Bodo
A1  - Zündorf, Albert
T1  - STAMP 4 NLP – an agile framework for rapid quality-driven NLP applications development
T2  - Quality of Information and Communications Technology. QUATIC 2021
N2  - The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
KW  - Machine learning
KW  - Process model
KW  - Natural language processing
Y1  - 2021
SN  - 978-3-030-85346-4
SN  - 978-3-030-85347-1
U6  - http://dx.doi.org/10.1007/978-3-030-85347-1_12
N1  - International Conference on the Quality of Information and Communications Technology, QUATIC 2021, 8-11 September, Algarve, Portugal
SP  - 156
EP  - 166
PB  - Springer
CY  - Cham
ER  -