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KGDAL: knowledge graph guided double attention LSTM for rolling mortality prediction for AKI-D patients

Authors:

Lucas Jing Liu,

Victor Ortiz-Soriano,

Javier A. Neyra,

Jin ChenAuthors Info & Claims

BCB '21: Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Article No.: 53, Pages 1 - 10

https://doi.org/10.1145/3459930.3469513

Published: 01 August 2021 Publication History

Abstract

With the rapid accumulation of electronic health record (EHR) data, deep learning (DL) models have exhibited promising performance on patient risk prediction. Recent advances have also demonstrated the effectiveness of knowledge graphs (KG) in providing valuable prior knowledge for further improving DL model performance. However, it is still unclear how KG can be utilized to encode highorder relations among clinical concepts and how DL models can make full use of the encoded concept relations to solve real-world healthcare problems and to interpret the outcomes. We propose a novel knowledge graph guided double attention LSTM model named KGDAL for rolling mortality prediction for critically ill patients with acute kidney injury requiring dialysis (AKI-D). KGDAL constructs a KG-based two-dimension attention in both time and feature spaces. In the experiment with two large healthcare datasets, we compared KGDAL with a variety of rolling mortality prediction models and conducted an ablation study to test the effectiveness, efficacy, and contribution of different attention mechanisms. The results showed that KGDAL clearly outperformed all the compared models. Also, KGDAL-derived patient risk trajectories may assist healthcare providers to make timely decisions and actions. The source code, sample data, and manual of KGDAL are available at https://github.com/lucasliu0928/KGDAL.

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Cited By

Fang CArango Argoty GKagiampakis IKhalid MJacob EBulusu KMarkuzon N(2024)Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancerJournal of Translational Medicine10.1186/s12967-024-05509-922:1Online publication date: 5-Aug-2024
https://doi.org/10.1186/s12967-024-05509-9
Wang ZLong HYu HTan L(2024)DKGC-LSTM:Fusion of Domain Knowledge to Guide CNN and LSTM for Heart Failure Risk Prediction2024 IEEE International Conference on Medical Artificial Intelligence (MedAI)10.1109/MedAI62885.2024.00078(552-557)Online publication date: 15-Nov-2024
https://doi.org/10.1109/MedAI62885.2024.00078
Oberste LHeinzl A(2023)User-Centric Explainability in Healthcare: A Knowledge-Level Perspective of Informed Machine LearningIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.32272254:4(840-857)Online publication date: Aug-2023
https://doi.org/10.1109/TAI.2022.3227225
Show More Cited By

Index Terms

KGDAL: knowledge graph guided double attention LSTM for rolling mortality prediction for AKI-D patients
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

BCB '21: Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 2021

603 pages

ISBN:9781450384506

DOI:10.1145/3459930

General Chairs:
Hongmei Jiang
Northwestern University
,
Xiuzhen Huang
Arkansas State University
,
Jiajie Zhang
The University of Texas Health Science Center at Houston

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 August 2021

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National Institute of Diabetes and Digestive and Kidney Diseases

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BCB '21

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SIGBIOM

BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 1 - 4, 2021

Florida, Gainesville

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Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Fang CArango Argoty GKagiampakis IKhalid MJacob EBulusu KMarkuzon N(2024)Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancerJournal of Translational Medicine10.1186/s12967-024-05509-922:1Online publication date: 5-Aug-2024
https://doi.org/10.1186/s12967-024-05509-9
Wang ZLong HYu HTan L(2024)DKGC-LSTM:Fusion of Domain Knowledge to Guide CNN and LSTM for Heart Failure Risk Prediction2024 IEEE International Conference on Medical Artificial Intelligence (MedAI)10.1109/MedAI62885.2024.00078(552-557)Online publication date: 15-Nov-2024
https://doi.org/10.1109/MedAI62885.2024.00078
Oberste LHeinzl A(2023)User-Centric Explainability in Healthcare: A Knowledge-Level Perspective of Informed Machine LearningIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.32272254:4(840-857)Online publication date: Aug-2023
https://doi.org/10.1109/TAI.2022.3227225
Do TYang HLee GKim SKho B(2023)Rapid Response System Based on Graph Attention Network for Predicting In-Hospital Clinical DeteriorationIEEE Access10.1109/ACCESS.2023.325740611(29091-29100)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3257406
Fathima Begum MNarayan S(2023)A pattern mixture model with long short-term memory network for acute kidney injury predictionJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.03.00735:4(172-182)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2023.03.007
Liu LOrtiz-Soriano VNeyra JChen J(2022)KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9994931(1086-1091)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9994931

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