Graph-based multi-label disease prediction model learning from medical data and domain knowledge

Pham, Thuan ORCID: https://orcid.org/0000-0001-7433-858X and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Zhang, Ji and Yong, Jianming and Li, Yuefeng and Xie, Haoran (2022) Graph-based multi-label disease prediction model learning from medical data and domain knowledge. Knowledge-Based Systems, 235:107662. pp. 1-15. ISSN 0950-7051


Abstract

In recent years, the means of disease diagnosis and treatment have been improved remarkably, along with the continuous development of technology and science. Researchers have spent tremendous time and effort to build models, with an aim to assist medical practitioners in decision-making support. One of the greatest challenges remains is how to identify the connection between different diseases. This study aims to discover the relationship between diseases and symptoms and predict potential diseases for patients. Considering it a multi-label classification problem, the study proposed a new multi-disease prediction model learning from NHANES, an extensive health related dataset, and MEDLINE, a corpus with medical domain knowledge. A heterogeneous information graph is firstly constructed and then populated using medical domain knowledge discovered from MEDLINE. The knowledge graph is analysed for clarification of the relevancy within nodes in positive or negative space, helping to access to the correlation amongst multiple diseases and their symptoms. A multi-label disease prediction model is then developed adopting the medical domain knowledge graph. Empirical experiments are conducted to evaluate the proposed model. The experimental results show that the performance of the proposed model surpassed state-of-the-art related works representing the mainstreams of multi-label classification. This study contributes with a novel model for multi-disease prediction to the medical community and represents a new endeavour on multi-label classification using knowledge graphs.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 03 Dec 2021 04:37
Last Modified: 03 Dec 2021 04:37
Uncontrolled Keywords: Multi-label classification; Knowledge graph; Medicine domain knowledge; Disease prediction; NHANES; MEDLINE
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0807 Library and Information Studies > 080702 Health Informatics
Fields of Research (2020): 42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems
46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460902 Decision support and group support systems
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
Socio-Economic Objectives (2008): B Economic Development > 89 Information and Communication Services > 8903 Information Services > 890301 Electronic Information Storage and Retrieval Services
E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions
Identification Number or DOI: https://doi.org/10.1016/j.knosys.2021.107662
URI: http://eprints.usq.edu.au/id/eprint/44202

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