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ConCarE: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

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ConCarE : Personalized Clinical Feature Embedding via Capturing the Healthcare Context. / Ma, Liantao; Zhang, Chaohe; Wang, Yasha; Ruan, Wenjie; Wang, Jiangtao; Tang, Wen; Ma, Xinyu; Gao, Xin; Gao, Junyi.

The Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020. AAAI, 2020. p. 833-840 (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 34, No. 1).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Ma, L, Zhang, C, Wang, Y, Ruan, W, Wang, J, Tang, W, Ma, X, Gao, X & Gao, J 2020, ConCarE: Personalized Clinical Feature Embedding via Capturing the Healthcare Context. in The Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020. Proceedings of the AAAI Conference on Artificial Intelligence, no. 1, vol. 34, AAAI, pp. 833-840. https://doi.org/10.1609/aaai.v34i01.5428

APA

Ma, L., Zhang, C., Wang, Y., Ruan, W., Wang, J., Tang, W., Ma, X., Gao, X., & Gao, J. (2020). ConCarE: Personalized Clinical Feature Embedding via Capturing the Healthcare Context. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020 (pp. 833-840). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 34, No. 1). AAAI. https://doi.org/10.1609/aaai.v34i01.5428

Vancouver

Ma L, Zhang C, Wang Y, Ruan W, Wang J, Tang W et al. ConCarE: Personalized Clinical Feature Embedding via Capturing the Healthcare Context. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020. AAAI. 2020. p. 833-840. (Proceedings of the AAAI Conference on Artificial Intelligence; 1). https://doi.org/10.1609/aaai.v34i01.5428

Author

Ma, Liantao ; Zhang, Chaohe ; Wang, Yasha ; Ruan, Wenjie ; Wang, Jiangtao ; Tang, Wen ; Ma, Xinyu ; Gao, Xin ; Gao, Junyi. / ConCarE : Personalized Clinical Feature Embedding via Capturing the Healthcare Context. The Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020. AAAI, 2020. pp. 833-840 (Proceedings of the AAAI Conference on Artificial Intelligence; 1).

Bibtex

@inproceedings{e685a2c049e54cfca56b34ce4d2090e6,
title = "ConCarE: Personalized Clinical Feature Embedding via Capturing the Healthcare Context",
abstract = "Predicting the patient{\textquoteright}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 vis- its. Although those works have shown superior performances in healthcare prediction, they fail to thoroughly explore the personal characteristics during the clinical visits. Moreover, existing work usually assumes that a more recent record has a larger weight in the prediction, but this assumption is not true for certain clinical features. 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 diversely captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. More importantly, ConCare is able to extract medical findings which can be confirmed by human experts and medical literature.",
author = "Liantao Ma and Chaohe Zhang and Yasha Wang and Wenjie Ruan and Jiangtao Wang and Wen Tang and Xinyu Ma and Xin Gao and Junyi Gao",
year = "2020",
month = apr,
day = "3",
doi = "10.1609/aaai.v34i01.5428",
language = "English",
isbn = "9781577358350",
series = "Proceedings of the AAAI Conference on Artificial Intelligence",
publisher = "AAAI",
number = "1",
pages = "833--840",
booktitle = "The Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020",

}

RIS

TY - GEN

T1 - ConCarE

T2 - Personalized Clinical Feature Embedding via Capturing the Healthcare Context

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 - 2020/4/3

Y1 - 2020/4/3

N2 - 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 vis- its. Although those works have shown superior performances in healthcare prediction, they fail to thoroughly explore the personal characteristics during the clinical visits. Moreover, existing work usually assumes that a more recent record has a larger weight in the prediction, but this assumption is not true for certain clinical features. 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 diversely captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. More importantly, ConCare is able to extract medical findings which can be confirmed by human experts and medical literature.

AB - 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 vis- its. Although those works have shown superior performances in healthcare prediction, they fail to thoroughly explore the personal characteristics during the clinical visits. Moreover, existing work usually assumes that a more recent record has a larger weight in the prediction, but this assumption is not true for certain clinical features. 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 diversely captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. More importantly, ConCare is able to extract medical findings which can be confirmed by human experts and medical literature.

U2 - 10.1609/aaai.v34i01.5428

DO - 10.1609/aaai.v34i01.5428

M3 - Conference contribution/Paper

SN - 9781577358350

T3 - Proceedings of the AAAI Conference on Artificial Intelligence

SP - 833

EP - 840

BT - The Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

PB - AAAI

ER -