Graph Machine Learning in Healthcare
A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5305
Special Issue Editor
Interests: health informatics; artificial intelligence; data science; complex networks; project analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With this Special Issue, we aim to create a collection of articles that will explore the application of different measures, methods and models of machine learning and network analytics (together named graph machine learning) to the healthcare decision-making process.
The principal goal of a well-structured healthcare system is to provide the best possible care to its consumers. Various healthcare stakeholders, including general practitioners, specialists, hospitals, radiology and image providers, pathology service providers, pharmacies, aged care facilities, and funders, work together to keep the whole healthcare system running smoothly. These healthcare entities generate a large amount of data, becoming valuable resources that help us to make evidence-based decisions and understand how the entire system is performing. Since these entities and the data they generate are inherently connected, graph machine learning has significant potential to offer insights into the hidden relationships across these data elements, which can eventually facilitate the process of the healthcare decision-making procedure.
Graph machine learning has emerged from using hand-crafted feature engineering (e.g., network centrality, centralisation, and density) to random-walk approaches (e.g., Node2Vec and Graph convolutional networks) and then graph neural networks. Due to its strong potential to reveal hidden insights of any network, various measures, methods and models of graph machine learning have gained wide-ranging acceptability and application in healthcare research in recent years.
In this Special Issue, we welcome the submission of methodological, empirical and review papers that use methods, measures and models of data analytics and have a clear implication for healthcare decision making. The submitted papers can be based on primary (e.g., based on study design) and/or secondary research data (e.g., administrative claim data and electronic medical records). Papers of a high academic standard addressing any healthcare decision-making issue using graph machine learning are invited for submission to this Special Issue.
Dr. Shahadat Uddin
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- network analytics
- healthcare
- health informatics
- administrative claim data
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