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Found 8 papers, 4 papers with code
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e. g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models fall short by forcing a tradeoff between accuracy and interpretability.
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components.
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies.
Ten Quick Tips for Deep Learning in Biology
In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling.
Hybrid Subspace Learning for High-Dimensional Data
One way to achieve this goal is to perform subspace learning to estimate a small set of latent features that capture the majority of the variance in the original data.
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data.
Towards Visual Explanations for Convolutional Neural Networks via Input Resampling
The predictive power of neural networks often costs model interpretability.
