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.
|Title of host publication||AAAI 2020 - 34th AAAI Conference on Artificial Intelligence|
|Publisher||AAAI Press / International Joint Conferences on Artificial Intelligence|
|Number of pages||8|
|Publication status||Published - 2020|
|Event||34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States|
Duration: 7 Feb 2020 → 12 Feb 2020
|Name||AAAI 2020 - 34th AAAI Conference on Artificial Intelligence|
|Conference||34th AAAI Conference on Artificial Intelligence, AAAI 2020|
|Period||7/02/20 → 12/02/20|
Bibliographical noteFunding Information:
This work is supported by the National Science and Technology Major Project (No. 2018ZX10201002), and the fund of the Peking University Health Science Center (BMU20160584). WR is supported by ORCA PRF Project (EP/R026173/1).
ASJC Scopus subject areas
- Artificial Intelligence