ijerph-logo

Journal Browser

Journal Browser

Health Information Systems: Advanced Machine Learning in Health Applications

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 10041

Special Issue Editors

School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Interests: health informatics; machine learning algorithms; telehealth; cyber security systems
School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: machine learning algorithms; data mining; ai-based data analysis in health applications

Special Issue Information

Dear Colleagues,

Machine learning algorithms are becoming increasingly popular in health applications, and are being used to improve the accuracy of predictions. Health professionals have started to rely on the outcomes of these algorithms in order to refine their diagnostic methods and associated treatments. These algorithms are now prevalent in health and pharmaceutical domains such as disease identification, classification and diagnostics, personalized medicines, behavioural motivations and smart health records. While many challenges still need to be overcome, the initial innovations demonstrated by machine learning algorithms in health applications have opened up a new domain of activity worldwide.

In this Special Issue, we call for innovative use of advanced machine learning algorithms in health applications—both theoretical and practical applications. Machine learning-driven health approaches characterized by advanced research methods, reviews, the development of innovative technologies, and the applications of these methods and technologies to a wide range of health consumers form part of this Special Issue. Both research and review papers addressing these topics are invited for this Special Issue, especially those combining a high academic standard coupled with a practical focus on providing solutions to both researchers and practitioners.

Prof. Dr. Raj Gururajan
Prof. Dr. Yuefeng Li
Guest Editors

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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

  • health information systems
  • machine learning
  • innovative algorithms
  • data mining
  • predictive modeling

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 3712 KiB  
Article
The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model
by Zhiyuan Hao, Jie Ma and Wenjing Sun
Int. J. Environ. Res. Public Health 2022, 19(19), 12509; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191912509 - 30 Sep 2022
Cited by 3 | Viewed by 1255
Abstract
The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively [...] Read more.
The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively applied in the medical and health field, where the construction of disease prediction models is an especially effective method to realize auxiliary medical diagnosis. However, the existing related studies mostly focus on the prediction analysis for a certain disease, using models with which it might be challenging to predict other diseases effectively. To address the issues existing in the aforementioned studies, this paper constructs four novel strategies to achieve a self-adaptive disease prediction process, i.e., the hunger-state foraging strategy of producers (PHFS), the parallel strategy for exploration and exploitation (EEPS), the perturbation–exploration strategy (PES), and the parameter self-adaptive strategy (PSAS), and eventually proposes a self-adaptive disease prediction model with applied universality, strong generalization ability, and strong robustness, i.e., multi-strategies optimization-based kernel extreme learning machine (MsO-KELM). Meanwhile, this paper selects six different real-world disease datasets as the experimental samples, which include the Breast Cancer dataset (cancer), the Parkinson dataset (Parkinson’s disease), the Autistic Spectrum Disorder Screening Data for Children dataset (Autism Spectrum Disorder), the Heart Disease dataset (heart disease), the Cleveland dataset (heart disease), and the Bupa dataset (liver disease). In terms of the prediction accuracy, the proposed MsO-KELM can obtain ACC values in analyzing these six diseases of 94.124%, 84.167%, 91.079%, 72.222%, 70.184%, and 70.476%, respectively. These ACC values have all been increased by nearly 2–7% compared with those obtained by the other models mentioned in this paper. This study deepens the connection between information technology and medical health by exploring the self-adaptive disease prediction model, which is an intuitive representation of digital health and could provide a scientific and reliable diagnostic basis for medical workers. Full article
Show Figures

Figure 1

13 pages, 405 KiB  
Article
Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques
by Chee Keong Wee, Xujuan Zhou, Ruiliang Sun, Raj Gururajan, Xiaohui Tao, Yuefeng Li and Nathan Wee
Int. J. Environ. Res. Public Health 2022, 19(12), 7384; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19127384 - 16 Jun 2022
Cited by 5 | Viewed by 2416
Abstract
Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled [...] Read more.
Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC’s updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training. Medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical referrals, which are unstructured free text. The proposed multiclass classification approach achieved a Micro F1 score = 0.98. The proposed approach can help in the processing of two million referrals that the QLD health service receives annually; therefore, they can deliver better and more efficient health services. Full article
Show Figures

Figure 1

17 pages, 846 KiB  
Article
Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus
by Mukkesh Kumar, Li Ting Ang, Hang Png, Maisie Ng, Karen Tan, See Ling Loy, Kok Hian Tan, Jerry Kok Yen Chan, Keith M. Godfrey, Shiao-yng Chan, Yap Seng Chong, Johan G. Eriksson, Mengling Feng and Neerja Karnani
Int. J. Environ. Res. Public Health 2022, 19(11), 6792; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19116792 - 01 Jun 2022
Cited by 5 | Viewed by 2829
Abstract
The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with [...] Read more.
The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A1c (HbA1c), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA1c was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13–1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12–2.38)). Optimal control of preconception HbA1c may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception. Full article
Show Figures

Figure 1

15 pages, 1525 KiB  
Article
LONG-REMI: An AI-Based Technological Application to Promote Healthy Mental Longevity Grounded in Reminiscence Therapy
by Àngela Nebot, Sara Domènech, Natália Albino-Pires, Francisco Mugica, Anass Benali, Xènia Porta, Oriol Nebot and Pedro M. Santos
Int. J. Environ. Res. Public Health 2022, 19(10), 5997; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19105997 - 15 May 2022
Cited by 6 | Viewed by 2473
Abstract
Reminiscence therapy (RT) consists of thinking about one’s own experiences through the presentation of memory-facilitating stimuli, and it has as its fundamental axis the activation of emotions. An innovative way of offering RT involves the use of technology-assisted applications, which must also satisfy [...] Read more.
Reminiscence therapy (RT) consists of thinking about one’s own experiences through the presentation of memory-facilitating stimuli, and it has as its fundamental axis the activation of emotions. An innovative way of offering RT involves the use of technology-assisted applications, which must also satisfy the needs of the user. This study aimed to develop an AI-based computer application that recreates RT in a personalized way, meeting the characteristics of RT guided by a therapist or a caregiver. The material guiding RT focuses on intangible cultural heritage. The application incorporates facial expression analysis and reinforcement learning techniques, with the aim of identifying the user’s emotions and, with them, guiding the computer system that emulates RT dynamically and in real time. A pilot study was carried out at five senior centers in Barcelona and Portugal. The results obtained are very positive, showing high user satisfaction. Moreover, the results indicate that the high frequency of positive emotions increased in the participants at the end of the intervention, while the low frequencies of negative emotions were maintained at the end of the intervention. Full article
Show Figures

Figure 1

Back to TopTop