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 .

本研究解决了现有机器学习模型在电子健康记录（EHR）中缺乏稳健表示学习及其解释性不足的问题。提出的DeepSelective框架通过整合数据压缩技术和创新的特征选择方法，不仅提高了预测精度，还显著增强了模型的可解释性，为临床决策提供了重要工具。

深度选择：用于可解释临床预测的特征门控和表示匹配