AIMEN trains an ensemble of neural networks on CTGAN-augmented data to predict adverse labor outcomes at 0.784 F1 and produces sparse counterfactual explanations identifying changes in two to three attributes.
In 2023 IEEE 19th International Conference on Body Sensor Networks (BSN)
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Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
AIMEN trains an ensemble of neural networks on CTGAN-augmented data to predict adverse labor outcomes at 0.784 F1 and produces sparse counterfactual explanations identifying changes in two to three attributes.