Application of rough sets in the presumptive diagnosis of urinary system diseases

By J. Czerniak, H. Zarzycki. 2003

108 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bernhard Schölkopf
2. Dominik Janzing
3. Jan Peters
4. Joris M. Mooij
5. Kun Zhang
6. David Lopez-Paz
7. Zachary Chase Lipton
8. Jakob Zscheischler

In Computational Theory and Mathematics

1. Bernhard Schölkopf
2. Jan Peters
3. Dominik Janzing
4. Joris M. Mooij
5. Kun Zhang
6. Jakob Zscheischler
7. Charles Peter Elkan
8. J. Janzing

Citations in the corpus

• Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “This data set, available at the UCI Machine Learning Repository (Bache and Lichman, 2013), was collected in order to create a computer expert system that decides whether a patient suffers from two different diseases of urinary system (Czerniak and Zarzycki, 2003).”
• Causal Inference on Discrete Data Using Additive Noise Models (2009, methodology): “The data set acute inflammations [5] from the UCI ML Repository [3] consists of 120 patients.”
• Differential Privacy and Machine Learning: a Survey and Review (2014, background): “Mechanisms that employ this idea include [42, 27].”
• Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “D22: Acute Inflammations This data set, available at the UCI Machine Learning Repository (Bache and Lichman, 2013), was collected in order to create a computer expert system that decides whether a patient suffers from two different diseases of urinary system (Czerniak and Zarzycki, 2003).”