Recent Advances in Computational Optimization Techniques and Their Modern Applications for Smart HealthCare

Topic Information

Dear Colleagues,

Medical and healthcare-related research is moving to new fronts in accuracy due to the growing number of data being acquired in healthcare systems and the convergence of diverse areas of knowledge in the health sector. Along with providing a rare chance and solid assurance for tackling various pressing issues, this trend also has technical applications of artificial intelligence (AI) and operations research (OR). Recently, AI techniques have been researched and used as potentially useful tools for the creation and implementation of intelligent systems inside the healthcare system. By taking into account the undeniable uncertainty of health data and procedures, AI-based systems can often learn from data and adapt according to real-time changes and fluctuations. There have been numerous attempts to date using a variety of techniques, including machine learning (ML), neural networks, optimization, computational intelligence, and human-machine interaction. This Topic Issue invites papers discussing recent advances in the development and application of Optimization, Machine Learning, and Artificial Intelligence in Healthcare.

Dr. Mohd Dilshad Ansari
Prof. Dr. Mohd Fauzi Bin Othman
Dr. Jawad Rasheed
Dr. Mazdak Zamani
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Healthcare healthcare	2.4	3.5	2013	20.3 Days	CHF 2700
Symmetry symmetry	2.2	5.4	2009	17.3 Days	CHF 2400

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Published Papers (4 papers)

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All Mathematics Applied Sciences Electronics Healthcare Symmetry

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16 pages, 4952 KiB  

Open AccessArticle

Exploring Acute Pancreatitis Clinical Pathways Using a Novel Process Mining Method

by Xue Yang, Wei Huang, Weiling Zhao, Xiaobo Zhou, Na Shi and Qing Xia

Healthcare 2023, 11(18), 2529; https://doi.org/10.3390/healthcare11182529 - 13 Sep 2023

Cited by 2 | Viewed by 1434

Abstract

Mining process models of medical behavior from electronic medical records is an effective way to optimize clinical pathways. However, clinical medical behavior is an extremely complex field with high nonlinearity and variability, and thus we need to adopt a more effective method. In [...] Read more.

Mining process models of medical behavior from electronic medical records is an effective way to optimize clinical pathways. However, clinical medical behavior is an extremely complex field with high nonlinearity and variability, and thus we need to adopt a more effective method. In this study, we developed a fuzzy process mining method for complex clinical pathways. Firstly, we designed a multi-level expert classification system with fuzzy values to preserve finer details. Secondly, we categorized medical events into long-term and temporary events for more specific data processing. Subsequently, we utilized electronic medical record (EMR) data of acute pancreatitis spanning 9 years, collected from a large general hospital in China, to evaluate the effectiveness of our method. The results demonstrated that our modeling process was simple and understandable, allowing for a more comprehensive representation of medical intricacies. Moreover, our method exhibited high patient coverage (>0.94) and discrimination (>0.838). These findings were corroborated by clinicians, affirming the accuracy and effectiveness of our approach. Full article

(This article belongs to the Topic Recent Advances in Computational Optimization Techniques and Their Modern Applications for Smart HealthCare)

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19 pages, 4687 KiB  

Open AccessArticle

A Data Quality Measurement Framework Using Distribution-Based Modeling and Simulation in Real-Time Telemedicine Systems

by Zoltán Richárd Jánki and Vilmos Bilicki

Appl. Sci. 2023, 13(13), 7548; https://doi.org/10.3390/app13137548 - 26 Jun 2023

Cited by 1 | Viewed by 1496

Abstract

In modern telemedicine systems, the data path can be exceedingly complex, with data passing through a number of phases and processes before reaching its ultimate state. It is extremely difficult to predict the effects of lacking data or improperly processed data on the [...] Read more.

In modern telemedicine systems, the data path can be exceedingly complex, with data passing through a number of phases and processes before reaching its ultimate state. It is extremely difficult to predict the effects of lacking data or improperly processed data on the final outcome. In real-time systems, and particularly in the field of telemedicine, it is crucial to rapidly identify and rectify issues in order to prevent the loss of large amounts of data and the degradation of data quality. A basic simulation is insufficient for a comprehensive examination of the system; instead, modeling approaches are required. However, a minor system’s state space can be immense. We present a methodology and a hybrid framework that incorporate simulation, emulation, and modeling in order to evaluate the state space and potential consequences of a sufficiently large system in a more targeted and condensed manner. In this paper, we demonstrate the structure and operation of our framework using an actively researched telemedicine use case, as well as how data quality can fluctuate and new anomalies can emerge if data is corrupted during an intermediate phase. According to our real-time Atrial Fibrillation (AF) and classification use case, data loss can be as high as 15%. Full article

(This article belongs to the Topic Recent Advances in Computational Optimization Techniques and Their Modern Applications for Smart HealthCare)

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18 pages, 4947 KiB  

Open AccessArticle

Advanced Deep Learning Approaches for Accurate Brain Tumor Classification in Medical Imaging

by Amena Mahmoud, Nancy Awadallah Awad, Najah Alsubaie, Syed Immamul Ansarullah, Mohammed S. Alqahtani, Mohamed Abbas, Mohammed Usman, Ben Othman Soufiene and Abeer Saber

Symmetry 2023, 15(3), 571; https://doi.org/10.3390/sym15030571 - 22 Feb 2023

Cited by 26 | Viewed by 4656

Abstract

A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. [...] Read more.

A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. However, not all brain tumors cause asymmetry. Some tumors may be located in areas that do not affect facial symmetry or head shape. Additionally, the asymmetry caused by a brain tumor may be subtle and not easily noticeable, especially in the early stages of the condition. Brain tumor classification using deep learning involves using artificial neural networks to analyze medical images of the brain and classify them as either benign (not cancerous) or malignant (cancerous). In the field of medical imaging, Convolutional Neural Networks (CNN) have been used for tasks such as the classification of brain tumors. These models can then be used to assist in the diagnosis of brain tumors in new cases. Brain tissues can be analyzed using magnetic resonance imaging (MRI). By misdiagnosing forms of brain tumors, patients’ chances of survival will be significantly lowered. Checking the patient’s MRI scans is a common way to detect existing brain tumors. This approach takes a long time and is prone to human mistakes when dealing with large amounts of data and various kinds of brain tumors. In our proposed research, Convolutional Neural Network (CNN) models were trained to detect the three most prevalent forms of brain tumors, i.e., Glioma, Meningioma, and Pituitary; they were optimized using Aquila Optimizer (AQO), which was used for the initial population generation and modification for the selected dataset, dividing it into 80% for the training set and 20% for the testing set. We used the VGG-16, VGG-19, and Inception-V3 architectures with AQO optimizer for the training and validation of the brain tumor dataset and to obtain the best accuracy of 98.95% for the VGG-19 model. Full article

(This article belongs to the Topic Recent Advances in Computational Optimization Techniques and Their Modern Applications for Smart HealthCare)

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19 pages, 1905 KiB  

Open AccessArticle

Sppn-Rn101: Spatial Pyramid Pooling Network with Resnet101-Based Foreign Object Debris Detection in Airports

by Abdulaziz Alshammari and Rakan C. Chabaan

Mathematics 2023, 11(4), 841; https://doi.org/10.3390/math11040841 - 7 Feb 2023

Cited by 8 | Viewed by 2807

Abstract

Over the past few years, aviation security has turned into a vital domain as foreign object debris (FOD) on the airport paved path possesses an enormous possible threat to airplanes at the time of takeoff and landing. Hence, FOD’s precise identification remains significant [...] Read more.

Over the past few years, aviation security has turned into a vital domain as foreign object debris (FOD) on the airport paved path possesses an enormous possible threat to airplanes at the time of takeoff and landing. Hence, FOD’s precise identification remains significant for assuring airplane flight security. The material features of FOD remain the very critical criteria for comprehending the destruction rate endured by an airplane. Nevertheless, the most frequent identification systems miss an efficient methodology for automated material identification. This study proffers a new FOD technique centered on transfer learning and also a mainstream deep convolutional neural network. For object detection (OD), this embraces the spatial pyramid pooling network with ResNet101 (SPPN-RN101), which assists in concatenating the local features upon disparate scales within a similar convolution layer with fewer position errors while identifying little objects. Additionally, Softmax with Adam Optimizer in CNN enhances the training speed with greater identification accuracy. This study presents FOD’s image dataset called FOD in Airports (FODA). In addition to the bounding boxes’ principal annotations for OD, FODA gives labeled environmental scenarios. Consequently, every annotation instance has been additionally classified into three light-level classes (bright, dim, and dark) and two weather classes (dry and wet). The proffered SPPN-ResNet101 paradigm is correlated to the former methodologies, and the simulation outcomes exhibit that the proffered study executes an AP medium of 0.55 for the COCO metric, 0.97 AP for the pascal metric, and 0.83 MAP of pascal metric. Full article

(This article belongs to the Topic Recent Advances in Computational Optimization Techniques and Their Modern Applications for Smart HealthCare)

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