Artificial Intelligence and Its Applications in Health Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 3169

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Guest Editor
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Interests: computational surgery; medical image analysis; biomechanical analysis; machine learning
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Special Issue Information

Dear Colleagues,

The emergence of artificial intelligence (AI)/machine learning resulted in dramatic improvements for systems engineering management, systems-based project planning in urban settings, health systems, environmental management and complex social systems. With recent advancements in health informatics, the health systems data have grown exponentially, and the nature of such big data is increasingly complex. There are many advantages to be gained with the application in healthcare of advances in machine learning and big data; however, there are many challenges in providing systems accurate enough to be useful to clinicians and patients. Not only are large amounts of data available, but sensitivity and specificity must be paid special attention, as well as ensuring support systems fit rationally into the health system. Recently, machine learning has been applied for effectively predicting the disease spread trends from public health data. Intelligent assisted computational models of user information preferences and interaction behaviors in health systems may also be designed. The aim of this Special Issue is to gather new advances in the use of artificial intelligence in health systems. We welcome both original research and review articles.

Dr. Guangming Zhang
Guest Editor

Manuscript Submission Information

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Keywords

• Health system
• Environmental Health
• Big data
• Machine learning
• Healthcare informatics
• Cluster and Classification
• Computational models.

Published Papers (1 paper)

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Research

19 pages, 5552 KiB  
Article
Coupled Projection Transfer Metric Learning for Cross-Session Emotion Recognition from EEG
by Fangyao Shen, Yong Peng, Guojun Dai, Baoliang Lu and Wanzeng Kong
Systems 2022, 10(2), 47; https://doi.org/10.3390/systems10020047 - 11 Apr 2022
Cited by 8 | Viewed by 2542
Abstract
Distribution discrepancies between different sessions greatly degenerate the performance of video-evoked electroencephalogram (EEG) emotion recognition. There are discrepancies since the EEG signal is weak and non-stationary and these discrepancies are manifested in different trails in each session and even in some trails which [...] Read more.
Distribution discrepancies between different sessions greatly degenerate the performance of video-evoked electroencephalogram (EEG) emotion recognition. There are discrepancies since the EEG signal is weak and non-stationary and these discrepancies are manifested in different trails in each session and even in some trails which belong to the same emotion. To this end, we propose a Coupled Projection Transfer Metric Learning (CPTML) model to jointly complete domain alignment and graph-based metric learning, which is a unified framework to simultaneously minimize cross-session and cross-trial divergences. By experimenting on the SEED_IV emotional dataset, we show that (1) CPTML exhibits a significantly better performance than several other approaches; (2) the cross-session distribution discrepancies are minimized and emotion metric graph across different trials are optimized in the CPTML-induced subspace, indicating the effectiveness of data alignment and metric exploration; and (3) critical EEG frequency bands and channels for emotion recognition are automatically identified from the learned projection matrices, providing more insights into the occurrence of the effect. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Health Systems)
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