Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explaining black-box text classifiers for disease-treatment information
  extraction   Deep neural networks and other intricate Artificial Intelligence (AI) models
have reached high levels of accuracy on many biomedical natural language
processing tasks. However, their applicability in real-world use cases may be
limited due to their vague inner working and decision logic. A post-hoc
explanation method can approximate the behavior of a black-box AI model by
extracting relationships between feature values and outcomes. In this paper, we
introduce a post-hoc explanation method that utilizes confident itemsets to
approximate the behavior of black-box classifiers for medical information
extraction. Incorporating medical concepts and semantics into the explanation
process, our explanator finds semantic relations between inputs and outputs in
different parts of the decision space of a black-box classifier. The
experimental results show that our explanation method can outperform
perturbation and decision set based explanators in terms of fidelity and
interpretability of explanations produced for predictions on a
disease-treatment information extraction task.

4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc