53 citations · 110 across the 8 of their papers we have counts for
8 papers
From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizations
Sven Weinzierl, Sandra Zilker, Annina Liessmann +3
Event logs reflect the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms these data into value by…
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Sven Kruschel, Nico Hambauer, Sven Weinzierl +3
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance ad…
Machine learning in business process management: A systematic literature review
Sven Weinzierl, Sandra Zilker, Sebastian Dunzer +1
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are u…
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
Sandra Zilker, Sven Weinzierl, Mathias Kraus +2
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage…
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
Patrick Zschech, Sven Weinzierl, Nico Hambauer +2
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the intern…
Prescriptive Business Process Monitoring for Recommending Next Best Actions
Sven Weinzierl, Sebastian Dunzer, Sandra Zilker +1
Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning t…
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
Sven Weinzierl, Sandra Zilker, Jens Brunk +3
Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve pro…
An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs
S. Weinzierl, S. Zilker, J. Brunk +5
Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially,…