activity
20202025
most citedPrescriptive Business Process Monitoring for Recommending Next Best Actions

53 citations · 110 across the 8 of their papers we have counts for

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Showing cs.LGShow all

6 papers · 1 filter

cs.LG2025

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…

cs.LG2024★ 2 cited

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…

cs.LG2024

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…

cs.LG2024

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…

cs.LG2022★ 10 cited

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…

cs.LG2020★ 17 cited

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,…