ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

TL;DR

ConCare is a novel personalized healthcare prediction model that captures individual patient characteristics by modeling feature interrelationships and healthcare context from irregular EMR data, outperforming existing methods.

Contribution

It introduces a personalized embedding approach that models feature interdependencies and time-aware distributions, enhancing healthcare prediction accuracy.

Findings

ConCare outperforms baseline models on real-world EMR datasets.

Medical insights from ConCare align with expert knowledge and literature.

The model effectively captures dynamic and static feature interdependencies.

Abstract

Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those works have shown superior performances in healthcare prediction, they fail to explore the personal characteristics during the clinical visits thoroughly. Moreover, existing works usually assume that the more recent record weights more in the prediction, but this assumption is not suitable for all conditions. In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. Our solution can embed the feature sequences separately by modeling the time-aware distribution. ConCare further improves the multi-head self-attention via the cross-head decorrelation, so that the inter-dependencies among dynamic features and static baseline information can be effectively captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. The medical findings extracted by ConCare are also empirically confirmed by human experts and medical literature.