# P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Records

arXiv: https://arxiv.org/abs/2508.08646 · HTML: https://mlsanity.com/paper/2508.08646/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Benchmarking machine learning models on multi-centre eICU critical care dataset](https://mlsanity.com/paper/1910.00964/) (2019)
- [Locally Sparse Neural Networks for Tabular Biomedical Data](https://mlsanity.com/paper/2106.06468/) (2021)
- [AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning](https://mlsanity.com/paper/2008.12001/) (2020)
- [Feature Selection Using Reinforcement Learning](https://mlsanity.com/paper/2101.09460/) (2021)
- [Curvature-based Feature Selection with Application in Classifying Electronic Health Records](https://mlsanity.com/paper/2101.03581/) (2021)
- [AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking](https://mlsanity.com/paper/2401.11250/) (2024)
- [Binary Feature Mask Optimization for Feature Selection](https://mlsanity.com/paper/2401.12644/) (2024)
- [Dynamic feature selection in medical predictive monitoring by reinforcement learning](https://mlsanity.com/paper/2405.19729/) (2024)
- [Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks](https://mlsanity.com/paper/2408.03151/) (2024)

## Abstract (arXiv)

Electronic Health Records (EHR) have revolutionized healthcare by digitizing patient data, improving accessibility, and streamlining clinical workflows. However, extracting meaningful insights from these complex and multimodal datasets remains a significant challenge for researchers. Traditional feature selection methods often struggle with the inherent sparsity and heterogeneity of EHR data, especially when accounting for patient-specific variations and feature costs in clinical applications. To address these challenges, we propose a novel personalized, online and cost-aware feature selection framework tailored specifically for EHR datasets. The features are aquired in an online fashion for individual patients, incorporating budgetary constraints and feature variability costs. The framework is designed to effectively manage sparse and multimodal data, ensuring robust and scalable performance in diverse healthcare contexts. A primary application of our proposed method is to support physicians' decision making in patient screening scenarios. By guiding physicians toward incremental acquisition of the most informative features within budget constraints, our approach aims to increase diagnostic confidence while optimizing resource utilization.