TY - JOUR AU - Ma, Liantao AU - Zhang, Chaohe AU - Wang, Yasha AU - Ruan, Wenjie AU - Wang, Jiangtao AU - Tang, Wen AU - Ma, Xinyu AU - Gao, Xin AU - Gao, Junyi PY - 2020/04/03 Y2 - 2024/03/28 TI - ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 01 SE - AAAI Technical Track: Applications DO - 10.1609/aaai.v34i01.5428 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5428 SP - 833-840 AB - <p>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.</p> ER -