research-article

Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining

Authors:

Guodong LongAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 3

Article No.: 31, Pages 1 - 21

https://doi.org/10.1145/3003729

Published: 06 March 2017 Publication History

Abstract

In the era of big data, a mechanism that can automatically annotate disease codes to patients’ records in the medical information system is in demand. The purpose of this work is to propose a framework that automatically annotates the disease labels of multi-source patient data in Intensive Care Units (ICUs). We extract features from two main sources, medical charts and notes. The Bag-of-Words model is used to encode the features. Unlike most of the existing multi-label learning algorithms that globally consider correlations between diseases, our model learns disease correlation locally in the patient data. To achieve this, we derive a local disease correlation representation to enrich the discriminant power of each patient data. This representation is embedded into a unified multi-label learning framework. We develop an alternating algorithm to iteratively optimize the objective function. Extensive experiments have been conducted on a real-world ICU database. We have compared our algorithm with representative multi-label learning algorithms. Evaluation results have shown that our proposed method has state-of-the-art performance in the annotation of multiple diagnostic codes for ICU patients. This study suggests that problems in the automated diagnosis code annotation can be reliably addressed by using a multi-label learning model that exploits disease correlation. The findings of this study will greatly benefit health care and management in ICU considering that the automated diagnosis code annotation can significantly improve the quality and management of health care for both patients and caregivers.

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Cited By

Uma KFrancis SSun WMoens M(2024)Towards Explainability in Automated Medical Code Prediction from Clinical RecordsIntelligent Systems and Applications10.1007/978-3-031-47718-8_40(593-637)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-47718-8_40
Chen JGuo CLu MDing S(2022)Unifying Diagnosis Identification and Prediction Method Embedding the Disease Ontology Structure From Electronic Medical RecordsFrontiers in Public Health10.3389/fpubh.2021.7938019Online publication date: 20-Jan-2022
https://doi.org/10.3389/fpubh.2021.793801
Kong XGao HShen GDuan GDas S(2022)FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of VehiclesIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30620539:1(197-206)Online publication date: Feb-2022
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Index Terms

Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 3

August 2017

372 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3058790

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Copyright © 2017 ACM.

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Association for Computing Machinery

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Publication History

Published: 06 March 2017

Accepted: 01 September 2016

Revised: 01 June 2016

Received: 01 October 2015

Published in TKDD Volume 11, Issue 3

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Cited By

Uma KFrancis SSun WMoens M(2024)Towards Explainability in Automated Medical Code Prediction from Clinical RecordsIntelligent Systems and Applications10.1007/978-3-031-47718-8_40(593-637)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-47718-8_40
Chen JGuo CLu MDing S(2022)Unifying Diagnosis Identification and Prediction Method Embedding the Disease Ontology Structure From Electronic Medical RecordsFrontiers in Public Health10.3389/fpubh.2021.7938019Online publication date: 20-Jan-2022
https://doi.org/10.3389/fpubh.2021.793801
Kong XGao HShen GDuan GDas S(2022)FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of VehiclesIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30620539:1(197-206)Online publication date: Feb-2022
https://doi.org/10.1109/TCSS.2021.3062053
Sadiq SAryani ADemartini GHua WIndulska MBurton-Jones AKhosravi HBenavides-Prado DSellis TSomeh IVaithianathan RWang SZhou X(2022)Information Resilience: the nexus of responsible and agile approaches to information useThe VLDB Journal10.1007/s00778-021-00720-231:5(1059-1084)Online publication date: 16-Jan-2022
https://doi.org/10.1007/s00778-021-00720-2
Zheng X(2022)Real-Time Simulation Support for Real-Time SystemsHandbook of Real-Time Computing10.1007/978-981-287-251-7_40(591-604)Online publication date: 9-Aug-2022
https://doi.org/10.1007/978-981-287-251-7_40
Wang MWang HLiu XMa XWang B(2021)Drug-Drug Interactions Prediction via Knowledge Graph and Text Embedding (Preprint)JMIR Medical Informatics10.2196/28277Online publication date: 28-Feb-2021
https://doi.org/10.2196/28277
Wang XHan JLi BPan XXu H(2021)Automatic ICD-10 Coding Based on Multi-Head Attention Mechanism and Gated Residual Network2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM52615.2021.9669625(536-543)Online publication date: 9-Dec-2021
https://doi.org/10.1109/BIBM52615.2021.9669625
Teng FYang WChen LHuang LXu Q(2020)Explainable Prediction of Medical Codes With Knowledge GraphsFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2020.008678Online publication date: 14-Aug-2020
https://doi.org/10.3389/fbioe.2020.00867
Teng FMa ZChen JXiao MHuang L(2020)Automatic Medical Code Assignment via Deep Learning Approach for Intelligent HealthcareIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2020.299693724:9(2506-2515)Online publication date: Sep-2020
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Chowdhury DBakshi SSa PMajhi B(2020)Wavelet energy feature based source camera identification for ear biometric imagesPattern Recognition Letters10.1016/j.patrec.2018.10.009130:C(139-147)Online publication date: 1-Feb-2020
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