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

TL;DR

This paper introduces P-CAFE, a personalized, cost-aware feature selection framework for EHR data that improves clinical decision-making by efficiently selecting informative features within resource constraints.

Contribution

The paper presents a novel online, personalized feature selection method that accounts for feature costs and patient-specific variations in EHR data.

Findings

Effective management of sparse, multimodal EHR data.

Supports resource-efficient, personalized patient screening.

Enhances diagnostic confidence with optimized feature acquisition.

Abstract

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.