DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
The rapid accumulation of Electronic Health Records (EHRs) has transformed
healthcare by providing valuable data that enhance clinical predictions and
diagnoses. While conventional machine learning models have proven effective,
they often lack robust representation learning and depend heavily on
expert-crafted features. Although deep learning offers powerful solutions, it
is often criticized for its lack of interpretability. To address these
challenges, we propose DeepSelective, a novel end to end deep learning
framework for predicting patient prognosis using EHR data, with a strong
emphasis on enhancing model interpretability. DeepSelective combines data
compression techniques with an innovative feature selection approach,
integrating custom-designed modules that work together to improve both accuracy
and interpretability. Our experiments demonstrate that DeepSelective not only
enhances predictive accuracy but also significantly improves interpretability,
making it a valuable tool for clinical decision-making. The source code is
freely available at http://www.healthinformaticslab.org/supp/resources.php .
