Informatics in Medicine Unlocked最新文献

Several imaging modalities and many signal recording techniques have been used to study the brain activities. Significant advancements in medical device technologies like electroencephalographs have provided conditions for recording neural information with high temporal resolution. These recordings can be used to calculate the connections between different brain areas. It has been proved that brain abnormalities affect the brain activity in different brain regions and the connectivity patterns between them would change as the result. This paper studies the electroencephalogram (EEG) functional connectivity methods and investigates the impacts of brain abnormalities on the brain functional connectivities. The effects of different brain abnormalities including stroke, depression, emotional disorders, epilepsy, attention deficit hyperactivity disorder (ADHD), autism, and Alzheimer's disease on functional connectivity of the EEG recordings have been explored in this study. The EEG-based metrics and network properties of different brain abnormalities have been discussed to have a comparison of the connectivities affected by each abnormality. Also, the effects of therapy and medical intake on the EEG functional connectivity network of each abnormality have been reviewed.

Introduction

The evaluation of e-prescribing systems' usability is crucial as they are integral to the quality of healthcare services. This study evaluates the usability of three e-prescribing systems and examines the impact of individual factors on system usability.

Method

The objective of this descriptive study was to assess the usability of e-prescribing systems (EP, Dinad, and Shafa) as perceived by 105 physicians from three clinics at Hormozgan University of Medical Sciences in Bandar Abbas, Iran. The data was collected using the 2020 edition of the Isometric Questionnaire 9241/110, which comprises of seven axes and 66 questions. The participants were asked to rate their opinions on a 5-point Likert scale, with options ranging from completely disagree [1] to completely agree [5].

Results

EP, Dinad, and Shafa received average scores of 3.45, 3.32, and 3.24, respectively. Self-descriptiveness and User Error Tolerance axes were rated the highest ratings, with average scores of 3.60 and 3.48. Conversely, conformity and suitability axes received the lowest ratings, with average scores of 3.19 and 3.22, respectively. Upon evaluating the usability axes, the EP significantly improved controllability and user engagement compared to other systems. The usability of Dinad and Shafa in the Gynecology clinic was significantly higher than the two other clinics. Also, older physicians with more work experience rated the Shafa significantly higher than two other systems.

Conclusion

The evaluated systems had average usability. although there was no statistically significant difference in the usability of these systems, the evaluation of dimensions revealed unique strengths in each system.

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25 % and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94 % in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation.

This study employs fractional, non-optimal, and optimal control techniques to analyze measles transmission dynamics using real-world data. Thus, we develop a fractional-order compartmental model capturing measles transmission dynamics. We then formulate an optimal control problem to minimize the disease burden while considering constraints such as vaccination resources and intervention costs. The proposed model’s positivity, boundedness, measles reproduction number, and stability are obtained. The sensitivity analysis using the partial rank correlation coefficient is shown for the fractional orders of 0.99 and 0.90. It is noticed that the rate of recruitment into the susceptible population ($\pi$), the rate at which individuals in the latent class become asymptomatic (${\alpha }_1$), and the transmission rate ($\beta$) contribute positively to the spread of the disease, while the rate at which individuals in the asymptomatic class become symptomatic (${\alpha }_2$), the vaccination rate for the first measles dose (${\gamma }_1$), and the rate at which individuals in the latent class recover from measles (${\delta }_1$) contribute significantly to the reduction of measles in the community. Utilizing numerical simulations and sensitivity analyses, we identify optimal control strategies that balance the trade-offs between intervention efficacy, resource allocation, and societal costs. Our findings provide insights into the effectiveness of fractional optimal control strategies in mitigating measles outbreaks and contribute to developing more robust and adaptive disease control policies in real-world scenarios.

Purpose

This research investigates the impact of four specific vaccines on the health of people who have been vaccinated. The vaccines under scrutiny are MERCK, MODERNA, PFIZER BioNTech, and JANSSEN.

Methods

The analysis considers a range of variables, including symptoms, mortality status, gender, age, number of vaccine doses, hospitalization status, and the number of days following vaccination. The methodology involves cross-tabulation analysis to establish connections between vaccinated individuals and the variables under examination. The dataset was compiled from the Centers for Disease Control and Prevention, encompassing roughly 65,000 cases and documenting over 40 distinct symptoms.

Results

The overall mortality rate among the vaccinated population is noteworthy. Notably, 40 different mild to severe symptoms were reported among vaccinated individuals. The research highlights the 10 most common symptoms experienced after vaccination. Females under 60 years of age constitute the majority of the dataset.

Conclusions

The vaccination-related mortality rate stands at approximately 3 % of those who received the vaccine, with the majority of cases occurring among individuals under the age of 60, who were not hospitalized and had received their initial vaccine dose.

The quality of ultrasound (US) imaging has been constrained by its limited contrast and resolution, inherent speckle noise, and the presence of other artifacts. Existing traditional and deep learning-based US denoising approaches have many limitations, such as reliance on manual parameter configurations, poor performance for unknown noise levels, the requirement for a large number of training data, and high computational expense. To address these challenges, we propose a novel Generative Adversarial Network (GAN) based denoiser. Capsule networks are utilized in both the generator and discriminator of the proposed GAN to capture intricate sparse features with less complexity. In addition, bias components are removed from all neurons of the generator to handle the unknown noise levels. A parallel attention module is also included in the proposed model to further enhance denoising performance. The proposed approach is trained in a semi-supervised manner and can thus be trained with fewer labeled images. Experimental evaluation on publicly available HC18 and BUSI datasets showed that the proposed approach achieved state-of-the-art denoising performance, with PSNR values of 33.86 and 34.16, and SSIM indices of 0.91 and 0.90, respectively. Moreover, experiments showed that the proposed approach is lightweight and more than twice as fast as similar denoisers.

Background

Method

Results

Conclusions

The optimization of the vaccination campaign and medication distribution in rural regions of Morocco conducted by the Ministry of Health can be significantly improved by employing metaheuristic algorithms in conjunction with a tour planning system. This research proposes the utilization of six metaheuristic algorithms: genetic algorithm, rat swarm optimization, whale optimization, spotted hyena optimizer, penguins search optimization, and particle swarm optimization, to determine the most efficient routes for equipped trucks carrying vaccines and medications. These algorithms consider critical field constraints, such as operating hours of vaccination centers, vaccine availability, and distances between centers while minimizing the overall journey duration. In addition, a comprehensive tour planning system is integrated into the optimization framework accounting for transportation costs such as fuel expenses and truck maintenance costs. By incorporating these factors, the Ministry of Health aims to achieve the maximum efficiency while minimizing the financial burden associated with the vaccination campaign in rural areas. The integration of metaheuristics and the tour planning system presents a robust and data-driven solution for the Ministry of Health to enhance the effectiveness of their vaccination and medication distribution campaigns in rural regions of Morocco. This approach not only minimizes costs but also improves overall efficiency by ensuring timely access to vaccines and medications for the rural population. The findings of this research contribute to the growing body of knowledge in the field of healthcare logistics optimization and provide valuable insights for policymakers and practitioners involved in similar campaigns worldwide.