| Prescriptive business process monitoring for recommending next best actions S Weinzierl, S Dunzer, S Zilker, M Matzner International conference on business process management, 193-209, 2020 | 100 | 2020 |
| Machine learning in business process management: A systematic literature review S Weinzierl, S Zilker, S Dunzer, M Matzner Expert Systems with Applications 253, 124181, 2024 | 70 | 2024 |
| The influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations J Ochmann, L Michels, S Zilker, V Tiefenbeck, S Laumer | 51 | 2020 |
| XNAP: making LSTM-based next activity predictions explainable by using LRP S Weinzierl, S Zilker, J Brunk, K Revoredo, M Matzner, J Becker International Conference on Business Process Management, 129-141, 2020 | 44 | 2020 |
| Challenging the performance-interpretability trade-off: an evaluation of interpretable machine learning models S Kruschel, N Hambauer, S Weinzierl, S Zilker, M Kraus, P Zschech Business & Information Systems Engineering, 1-25, 2025 | 39 | 2025 |
| Bringing light into the darkness-A systematic literature review on explainable predictive business process monitoring techniques M Stierle, J Brunk, S Weinzierl, S Zilker, M Matzner, J Becker | 33 | 2021 |
| GAM (e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints P Zschech, S Weinzierl, N Hambauer, S Zilker, M Kraus arXiv preprint arXiv:2204.09123, 2022 | 30 | 2022 |
| The evaluation of the black box problem for AI-based recommendations: An interview-based study J Ochmann, S Zilker, S Laumer International conference on wirtschaftsinformatik, 232-246, 2021 | 28 | 2021 |
| A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs. S Weinzierl, M Stierle, S Zilker, M Matzner HICSS, 1-10, 2020 | 25 | 2020 |
| An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs S Weinzierl, S Zilker, J Brunk, K Revoredo, A Nguyen, M Matzner, ... arXiv preprint arXiv:2005.01194, 2020 | 24 | 2020 |
| A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis S Zilker, S Weinzierl, M Kraus, P Zschech, M Matzner Health Care Management Science 27 (2), 136-167, 2024 | 23 | 2024 |
| From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events. S Weinzierl, S Zilker, M Stierle, M Matzner, G Park Wirtschaftsinformatik (Zentrale Tracks), 364-368, 2020 | 22 | 2020 |
| Text-aware predictive process monitoring with contextualized word embeddings L Cabrera, S Weinzierl, S Zilker, M Matzner International Conference on Business Process Management, 303-314, 2022 | 17 | 2022 |
| The status quo of process mining in the industrial sector S Dunzer, S Zilker, E Marx, V Grundler, M Matzner International Conference on Wirtschaftsinformatik, 629-644, 2021 | 16 | 2021 |
| Predictive business process deviation monitoring S Weinzierl, S Dunzer, JC Tenschert, S Zilker, M Matzner | 13 | 2021 |
| Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction S Zilker, S Weinzierl, P Zschech, M Kraus, M Matzner | 9 | 2023 |
| Design principles for comprehensible process discovery in process mining M Stierle, S Zilker, S Dunzer, JC Tenscher, G Karagegova | 8 | 2020 |
| Towards automated business process redesign in runtime using generative machine learning MV Harl, S Zilker, S Weinzierl | 6 | 2024 |
| Transfer learning for predictive process monitoring A Liessmann, W Wang, S Weinzierl, S Zilker, M Matzner | 6 | 2024 |
| Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models A Liessmann, S Zilker, S Weinzierl, M Sukhareva, M Matzner | 6 | 2024 |
Martin MatznerProfessor of Business Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)Verified email at fau.de
