Michael Schrempf

According to our database, Michael Schrempf authored at least 8 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study.

Diether Kramer, Stefanie Jauk, Sai Veeranki, Michael Schrempf, Julia Traub, Eva Kugel, Anna Prisching, Sandra Domnanich, Maria Leopold, Peter Krisper, Gerald Sendlhofer

Proceedings of the dHealth 2024, 2024

2023

Frail People in LABLand: Development of an Easy-to-Use Machine Learning Model to Identify Frail People in Hospitals Based on Laboratory Data.

Julian Gutheil, Philip Stampfer, Diether Kramer, Manuel Wechselberger, Sai Pavan Kumar Veeranki, Michael Schrempf, Peter Mrak, Martina Aubel, Franz Feichtner

Proceedings of the dHealth 2023, 2023

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events for ELGA-Authorized Clinics.

Seda Polat Erdeniz, Diether Kramer, Michael Schrempf, Peter P. Rainer, Alexander Felfernig, Thi Ngoc Trang Tran, Tamim Burgstaller, Sebastian Lubos

Proceedings of the dHealth 2023, 2023

Employing Nudge Theory and Persuasive Principles with Explainable AI in Clinical Decision Support.

Seda Polat Erdeniz, Thi Ngoc Trang Tran, Alexander Felfernig, Sebastian Lubos, Michael Schrempf, Diether Kramer, Peter P. Rainer

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023

Computational Evaluation of Model-Agnostic Explainable AI Using Local Feature Importance in Healthcare.

Seda Polat Erdeniz, Michael Schrempf, Diether Kramer, Peter P. Rainer, Alexander Felfernig, Thi Ngoc Trang Tran, Tamim Burgstaller, Sebastian Lubos

Proceedings of the Artificial Intelligence in Medicine, 2023

2022

A Comparative Study: Classification Vs. Matrix Factorization for Therapeutics Recommendation.

Seda Polat Erdeniz, Michael Schrempf, Diether Kramer, Alexander Felfernig

Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Development of an Architecture to Implement Machine Learning Based Risk Prediction in Clinical Routine: A Service-Oriented Approach.

Michael Schrempf, Seda Polat Erdeniz, Diether Kramer, Stefanie Jauk, Sai Pavan Kumar Veeranki, Werner Ribitsch, Werner Leodolter, Peter P. Rainer

Proceedings of the dHealth 2022, 2022

2021

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events.

Michael Schrempf, Diether Kramer, Stefanie Jauk, Sai Pavan Kumar Veeranki, Werner Leodolter, Peter P. Rainer

Proceedings of the Navigating Healthcare Through Challenging Times - Proceedings of dHealth 2021, 2021