• DOI:10.1007/978-3-030-24409-5_9
Corpus ID: 201125858

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}
}

• Cecilia Panigutti, Riccardo Guidotti, +1 author D. Pedreschi
• Published in Precision Health and Medicine 2020
• Computer Science

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

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