用深度学习提升病历预测准确率与可解释性
DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data
- 融合特征选择与数据压缩，自动筛选关键医疗指标
- 在多个真实病历数据集上预测准确率显著提升
- 适合临床医生需要透明决策支持的场景
电子健康记录（EHR）的快速增长为临床预测和诊断提供了宝贵数据。传统机器学习模型虽有效，但依赖人工特征且缺乏鲁棒表征学习能力。深度学习虽具强大性能，却常因不可解释性受质疑。为此，我们提出DeepSelective——一种端到端深度学习框架，用于基于EHR数据的患者预后预测，重点提升模型可解释性。该框架结合数据压缩与创新特征选择方法，集成定制模块协同优化准确率与可解释性。实验表明，DeepSelective不仅提升预测性能，更显著增强可解释性，是临床决策支持的有力工具。源代码可在http://www.healthinformaticslab.org/supp/resources.php免费获取。
原文摘要 · Abstract (English)
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 .
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