1 code implementation • Health Care Management Science 2024 • Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech, Martin Matzner
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources.
1 code implementation • 17 Mar 2024 • Theodor Stoecker, Nico Hambauer, Patrick Zschech, Mathias Kraus
In this paper, we propose IGANN Sparse, a novel machine learning model from the family of generalized additive models, which promotes sparsity through a non-linear feature selection process during training.
1 code implementation • 13 Feb 2024 • Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash, Markus Leippold
One avenue in reaching this goal is basing the answers on reliable sources.
1 code implementation • 15 Jan 2024 • Daniel Tschernutter, Mathias Kraus, Stefan Feuerriegel
Furthermore, we mathematically analyze the convergence rate of parameters and the convergence rate in value (i. e., the training loss).
no code implementations • 12 Oct 2023 • Tobias Schimanski, Julia Bingler, Camilla Hyslop, Mathias Kraus, Markus Leippold
Public and private actors struggle to assess the vast amounts of information about sustainability commitments made by various institutions.
no code implementations • 14 Aug 2023 • Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk.
1 code implementation • 28 Jul 2023 • Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold
In the face of climate change, are companies really taking substantial steps toward more sustainable operations?
no code implementations • 27 Jun 2023 • Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold
This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations.
no code implementations • 11 Apr 2023 • Saeid Ashraf Vaghefi, Qian Wang, Veruska Muccione, Jingwei Ni, Mathias Kraus, Julia Bingler, Tobias Schimanski, Chiara Colesanti-Senni, Nicolas Webersinke, Christrian Huggel, Markus Leippold
The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high).
no code implementations • 31 Mar 2023 • Mathias Kraus, Julia Anna Bingler, Markus Leippold, Tobias Schimanski, Chiara Colesanti Senni, Dominik Stammbach, Saeid Ashraf Vaghefi, Nicolas Webersinke
Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability in generating human-like text across diverse topics.
1 code implementation • 1 Sep 2022 • Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler, Mathias Kraus, Markus Leippold
To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable.
1 code implementation • 10 May 2022 • Daniel Hershcovich, Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, Markus Leippold
We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact.
2 code implementations • 19 Apr 2022 • Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models.
1 code implementation • 6 Jan 2022 • Maximilian Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV).
1 code implementation • 22 Oct 2021 • Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, Markus Leippold
Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP).
1 code implementation • 9 Feb 2021 • Yilmazcan Özyurt, Mathias Kraus, Tobias Hatt, Stefan Feuerriegel
In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs.
no code implementations • 17 Jul 2019 • Martin Maritsch, Caterina Bérubé, Mathias Kraus, Vera Lehmann, Thomas Züger, Stefan Feuerriegel, Tobias Kowatsch, Felix Wortmann
The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV).
no code implementations • 11 Jul 2019 • Mathias Kraus, Stefan Feuerriegel
This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.
1 code implementation • 24 May 2019 • Mathias Kraus, Stefan Feuerriegel
In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers.
no code implementations • 28 Jun 2018 • Mathias Kraus, Stefan Feuerriegel, Asil Oztekin
(4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning.
no code implementations • 16 Mar 2018 • Bernhard Kratzwald, Suzana Ilic, Mathias Kraus, Stefan Feuerriegel, Helmut Prendinger
Emotions widely affect human decision-making.
2 code implementations • 11 Oct 2017 • Mathias Kraus, Stefan Feuerriegel
Hence, this paper studies the use of deep neural networks for financial decision support.
no code implementations • 18 Apr 2017 • Mathias Kraus, Stefan Feuerriegel
To learn from the resulting rhetorical structure, we propose a tensor-based, tree-structured deep neural network (named Discourse-LSTM) in order to process the complete discourse tree.