Summary of Use Of What-if Scenarios to Help Explain Artificial Intelligence Models For Neonatal Health, by Abdullah Mamun et al.
Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
by Abdullah Mamun, Lawrence D. Devoe, Mark I. Evans, David W. Britt, Judith Klein-Seetharaman, Hassan Ghasemzadeh
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As a machine learning educator, I can summarize the abstract as follows: AIMEN is a deep learning framework that predicts adverse labor outcomes from various risk factors and provides explanations for its predictions. The framework uses an ensemble of fully-connected neural networks and addresses challenges like imbalance and small datasets through data augmentation techniques like ADASYN and CTGAN. AIMEN outperforms other models in classification and provides counterfactual explanations by modifying 2-3 attributes on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AIMEN is a new way to predict and prevent serious problems during childbirth. The goal is to identify risks early, so doctors can take action to help the baby. Right now, there’s no good computer system that can do this job accurately. AIMEN uses special computer programs called deep learning models to analyze different factors that might affect the birth outcome. It not only makes a prediction but also explains why it thinks something might go wrong. This can help doctors make better decisions and potentially prevent serious problems like cerebral palsy. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Deep learning » Machine learning
