KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction

Lucas Jing Liu; Victor Ortiz-Soriano; Javier A. Neyra; Jin Chen

doi:10.1109/BIBM55620.2022.9994931

KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction

Lucas Jing Liu, Victor Ortiz-Soriano, Javier A. Neyra, Jin Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Rapid accumulation of temporal Electronic Health Record (EHR) data and recent advances in deep learning have shown high potential in precisely and timely predicting patients' risks using AI. However, most existing risk prediction approaches ignore the complex asynchronous and irregular problems in real-world EHR data. This paper proposes a novel approach called Knowledge-guIded Time-aware LSTM (KIT-LSTM) for continuous mortality predictions using EHR. KIT-LSTM extends LSTM with two time-aware gates and a knowledge-aware gate to better model EHR and interprets results. Experiments on real-world data for patients with acute kidney injury with dialysis (AKI-D) demonstrate that KIT-LSTM performs better than the state-of-the-art methods for predicting patients' risk trajectories and model interpretation. KIT-LSTM can better support timely decision-making for clinicians.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Editors	Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
Pages	1086-1091
Number of pages	6
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9994931
State	Published - 2022
Event	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States Duration: Dec 6 2022 → Dec 8 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Country/Territory	United States
City	Las Vegas
Period	12/6/22 → 12/8/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Acute Kidney Injury
Continuous Prediction
Deep Learning
Electronic Health Record
Machine learning

ASJC Scopus subject areas

Psychiatry and Mental health
Information Systems and Management
Biomedical Engineering
Medicine (miscellaneous)
Cardiology and Cardiovascular Medicine
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM55620.2022.9994931

Cite this

Liu, L. J., Ortiz-Soriano, V., Neyra, J. A., & Chen, J. (2022). KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction. In D. Adjeroh, Q. Long, X. Shi, F. Guo, X. Hu, S. Aluru, G. Narasimhan, J. Wang, M. Kang, A. M. Mondal, & J. Liu (Eds.), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (pp. 1086-1091). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). https://doi.org/10.1109/BIBM55620.2022.9994931

Liu, Lucas Jing ; Ortiz-Soriano, Victor ; Neyra, Javier A. et al. / KIT-LSTM : Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. editor / Donald Adjeroh ; Qi Long ; Xinghua Shi ; Fei Guo ; Xiaohua Hu ; Srinivas Aluru ; Giri Narasimhan ; Jianxin Wang ; Mingon Kang ; Ananda M. Mondal ; Jin Liu. 2022. pp. 1086-1091 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@inproceedings{65c5436784b649988b8388cd1b92e4d3,

title = "KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction",

abstract = "Rapid accumulation of temporal Electronic Health Record (EHR) data and recent advances in deep learning have shown high potential in precisely and timely predicting patients' risks using AI. However, most existing risk prediction approaches ignore the complex asynchronous and irregular problems in real-world EHR data. This paper proposes a novel approach called Knowledge-guIded Time-aware LSTM (KIT-LSTM) for continuous mortality predictions using EHR. KIT-LSTM extends LSTM with two time-aware gates and a knowledge-aware gate to better model EHR and interprets results. Experiments on real-world data for patients with acute kidney injury with dialysis (AKI-D) demonstrate that KIT-LSTM performs better than the state-of-the-art methods for predicting patients' risk trajectories and model interpretation. KIT-LSTM can better support timely decision-making for clinicians.",

keywords = "Acute Kidney Injury, Continuous Prediction, Deep Learning, Electronic Health Record, Machine learning",

author = "Liu, {Lucas Jing} and Victor Ortiz-Soriano and Neyra, {Javier A.} and Jin Chen",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/BIBM55620.2022.9994931",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

pages = "1086--1091",

editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, {Ananda M.} and Jin Liu",

booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

}

Liu, LJ, Ortiz-Soriano, V, Neyra, JA & Chen, J 2022, KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction. in D Adjeroh, Q Long, X Shi, F Guo, X Hu, S Aluru, G Narasimhan, J Wang, M Kang, AM Mondal & J Liu (eds), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, pp. 1086-1091, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, United States, 12/6/22. https://doi.org/10.1109/BIBM55620.2022.9994931

KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction. / Liu, Lucas Jing; Ortiz-Soriano, Victor; Neyra, Javier A. et al.
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. ed. / Donald Adjeroh; Qi Long; Xinghua Shi; Fei Guo; Xiaohua Hu; Srinivas Aluru; Giri Narasimhan; Jianxin Wang; Mingon Kang; Ananda M. Mondal; Jin Liu. 2022. p. 1086-1091 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - KIT-LSTM

T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

AU - Liu, Lucas Jing

AU - Ortiz-Soriano, Victor

AU - Neyra, Javier A.

AU - Chen, Jin

PY - 2022

Y1 - 2022

N2 - Rapid accumulation of temporal Electronic Health Record (EHR) data and recent advances in deep learning have shown high potential in precisely and timely predicting patients' risks using AI. However, most existing risk prediction approaches ignore the complex asynchronous and irregular problems in real-world EHR data. This paper proposes a novel approach called Knowledge-guIded Time-aware LSTM (KIT-LSTM) for continuous mortality predictions using EHR. KIT-LSTM extends LSTM with two time-aware gates and a knowledge-aware gate to better model EHR and interprets results. Experiments on real-world data for patients with acute kidney injury with dialysis (AKI-D) demonstrate that KIT-LSTM performs better than the state-of-the-art methods for predicting patients' risk trajectories and model interpretation. KIT-LSTM can better support timely decision-making for clinicians.

AB - Rapid accumulation of temporal Electronic Health Record (EHR) data and recent advances in deep learning have shown high potential in precisely and timely predicting patients' risks using AI. However, most existing risk prediction approaches ignore the complex asynchronous and irregular problems in real-world EHR data. This paper proposes a novel approach called Knowledge-guIded Time-aware LSTM (KIT-LSTM) for continuous mortality predictions using EHR. KIT-LSTM extends LSTM with two time-aware gates and a knowledge-aware gate to better model EHR and interprets results. Experiments on real-world data for patients with acute kidney injury with dialysis (AKI-D) demonstrate that KIT-LSTM performs better than the state-of-the-art methods for predicting patients' risk trajectories and model interpretation. KIT-LSTM can better support timely decision-making for clinicians.

KW - Acute Kidney Injury

KW - Continuous Prediction

KW - Deep Learning

KW - Electronic Health Record

KW - Machine learning

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M3 - Conference contribution

AN - SCOPUS:85146699653

T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

SP - 1086

EP - 1091

BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

A2 - Adjeroh, Donald

A2 - Long, Qi

A2 - Shi, Xinghua

A2 - Guo, Fei

A2 - Hu, Xiaohua

A2 - Aluru, Srinivas

A2 - Narasimhan, Giri

A2 - Wang, Jianxin

A2 - Kang, Mingon

A2 - Mondal, Ananda M.

A2 - Liu, Jin

Y2 - 6 December 2022 through 8 December 2022

ER -

Liu LJ, Ortiz-Soriano V, Neyra JA, Chen J. KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction. In Adjeroh D, Long Q, Shi X, Guo F, Hu X, Aluru S, Narasimhan G, Wang J, Kang M, Mondal AM, Liu J, editors, Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. 2022. p. 1086-1091. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9994931

KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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