Special Issue "Computing and Artificial Intelligence Techniques for Healthcare Applications 2.0"

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: 31 December 2022.

Special Issue Editor

Dr. Ahmed Farouk
E-Mail Website
Guest Editor
Department of Physics and Computer Science, Faculty of Science, Wilfrid Laurier University, Waterloo, ON, Canada
Interests: quantum information and computation; information security and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid growth of biological data in recent years, data-driven computational methods are increasingly needed to analyze large-scale biological data quickly and accurately. Biological and medical technologies in particular have been providing us with explosive volumes of biological and physiological data, such as medical images, electroencephalography signals, and genomic and protein sequences. Learning from these data will facilitate our understanding of human health and disease. Accordingly, computation and machine learning techniques have recently emerged in both academia and industry as “intelligent” methods in many specific healthcare areas to gain insight from medical and biological data. To expand the scope and ease of the applicability of machine learning, it is highly desirable to make learning algorithms less dependent on handcrafted feature engineering, so that novel applications can be constructed faster and, more importantly, progress toward artificial intelligence (AI) can be made.

This Special Issue aims to target recent computation and machine learning techniques as well as state-of-the-art applications in healthcare areas such as bioinformatics, bioprocess systems, biomedical systems, biomedical physics, and bioecological systems. This Special Issue will consider original research articles and review articles on computational and intelligent methods in healthcare and their applications. We wish to gather relevant contributions introducing new techniques for the study of complex healthcare systems driven by computational methods. Papers on interdisciplinary applications are particularly welcome. We also encourage authors to make their codes and experimental data available to the public, so that our Special Issue can be more infusive and attractive.

Dr. Ahmed Farouk
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 papers will be 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 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 1800 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

  • quantum machine learning
  • supervised learning algorithms
  • unsupervised learning algorithms
  • imbalanced learning algorithms
  • multiview feature learning
  • deep-learning-based feature learning strategies
  • feature representation optimization algorithms
  • handcrafted feature representation algorithms
  • computational and mathematical techniques
  • image and signal processing
  • bioinformatics
  • mental health
  • bioprocess systems
  • biomedical systems
  • biomedical physics
  • bioecological systems

Published Papers (2 papers)

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

Research

Article
Solving Patient Allocation Problem during an Epidemic Dengue Fever Outbreak by Mathematical Modelling
Healthcare 2022, 10(1), 163; https://doi.org/10.3390/healthcare10010163 - 15 Jan 2022
Viewed by 137
Abstract
Dengue fever is a mosquito-borne disease that has rapidly spread throughout the last few decades. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns. However, appropriate mechanisms of response are indispensable to face [...] Read more.
Dengue fever is a mosquito-borne disease that has rapidly spread throughout the last few decades. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns. However, appropriate mechanisms of response are indispensable to face the consequent events when an outbreak takes place. This study applied single and multiple objective linear programming models to optimize the allocation of patients and additional resources during an epidemic dengue fever outbreak, minimizing the summation of the distance travelled by all patients. An empirical study was set in Ciudad del Este, Paraguay. Data provided by a privately run health insurance cooperative was used to verify the applicability of the models in this study. The results can be used by analysts and decision makers to solve patient allocation problems for providing essential medical care during an epidemic dengue fever outbreak. Full article
Show Figures

Figure 1

Article
Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data
Healthcare 2022, 10(1), 109; https://doi.org/10.3390/healthcare10010109 - 06 Jan 2022
Viewed by 161
Abstract
In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of [...] Read more.
In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets. Full article
Show Figures

Figure 1

Back to TopTop