89 citations · 267 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
An Nguyen, Srijeet Chatterjee, Sven Weinzierl +3
Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next…
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks
Matthias Stierle, Sven Weinzierl, Maximilian Harl +1
Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models…
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,…