Abstract
Qualitative influence statements are often provided a priori to guide learning; we answer a challenging reverse task and automatically extract them from a learned probabilistic model. We apply our Qualitative Knowledge Extraction method toward early prediction of gestational diabetes on clinical study data. Our empirical results demonstrate that the extracted rules are both interpretable and valid.
A. Karanam and A. L. Hayes—Equal contribution.
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Notes
- 1.
Without loss of generality, assume the variables in synergistic relation have monotonically increasing impact.
- 2.
Refer to the supplementary material for details on the data and features: https://starling.utdallas.edu/papers/QuaKE/.
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Acknowledgements
We gratefully acknowledge the support of 1R01HD101246 from NICHD and Precision Health Initiative of Indiana University. Thanks to Rashika Ramola, Rafael Guerrero for data processing and discussions.
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Karanam, A., Hayes, A.L., Kokel, H., Haas, D.M., Radivojac, P., Natarajan, S. (2021). A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_59
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