Explaining Multi-label Black-Box Classifiers for Health Applications

@inproceedings{Panigutti2020ExplainingMB,
  title={Explaining Multi-label Black-Box Classifiers for Health Applications},
  author={Cecilia Panigutti and Riccardo Guidotti and A. Monreale and D. Pedreschi},
  booktitle={Precision Health and Medicine},
  year={2020}
}
Today the state-of-the-art performance in classification is achieved by the so-called “black boxes”, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually… Expand
6 Citations

Figures, Tables, and Topics from this paper

Doctor XAI: an ontology-based approach to black-box sequential data classification explanations
  • 12
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
  • 20
  • PDF
Explaining Any Time Series Classifier
  • 1
Multiobjective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification
  • PDF
GLocalX - From Local to Global Explanations of Black Box AI Models
  • 2
  • PDF

References

SHOWING 1-10 OF 31 REFERENCES
A Survey of Methods for Explaining Black Box Models
  • 953
  • PDF
Interpretable Deep Models for ICU Outcome Prediction
  • 151
  • PDF
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
  • 4,204
  • PDF
Decision trees for hierarchical multi-label classification
  • 501
  • PDF
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
  • 424
  • PDF
Deep Computational Phenotyping
  • 185
  • PDF
...
1
2
3
4
...