1,084 research outputs found
Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study
Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring-in a very short time-of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). The length of stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
Background: Cardiovascular diseases (CVDs) continue to be the leading cause
of mortality on a global scale. In recent years, the application of artificial
intelligence (AI) techniques, particularly deep learning (DL), has gained
considerable popularity for evaluating the various aspects of CVDs. Moreover,
using fundus images and optical coherence tomography angiography (OCTA) to
diagnose retinal diseases has been extensively studied. To better understand
heart function and anticipate changes based on microvascular characteristics
and function, researchers are currently exploring the integration of AI with
non-invasive retinal scanning. Leveraging AI-assisted early detection and
prediction of cardiovascular diseases on a large scale holds excellent
potential to mitigate cardiovascular events and alleviate the economic burden
on healthcare systems. Method: A comprehensive search was conducted across
various databases, including PubMed, Medline, Google Scholar, Scopus, Web of
Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related
to cardiovascular diseases and artificial intelligence. Results: A total of 87
English-language publications, selected for relevance were included in the
study, and additional references were considered. This study presents an
overview of the current advancements and challenges in employing retinal
imaging and artificial intelligence to identify cardiovascular disorders and
provides insights for further exploration in this field. Conclusion:
Researchers aim to develop precise disease prognosis patterns as the aging
population and global CVD burden increase. AI and deep learning are
transforming healthcare, offering the potential for single retinal image-based
diagnosis of various CVDs, albeit with the need for accelerated adoption in
healthcare systems.Comment: 40 pages, 5 figures, 2 tables, 91 reference
Hybrid Modelling and Simulation (M&S): Driving Innovation in the Theory and Practice of M&S
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordHybrid Simulation (HS) is the application of two or more simulation techniques (e.g., ABS, DES, SD) in a
single M&S study. Distinct from HS, Hybrid Modelling (HM) is defined as the combined application of
simulation approaches (including HS) with methods and techniques from the broader OR/MS literature and
also across disciplines. In this paper, we expand on the unified conceptual representation and classification
of hybrid M&S, which includes both HS (Model Types A-C), hybrid OR/MS models (D, D.1) and crossdisciplinary hybrid models (Type E), and assess their innovation potential. We argue that model types
associated with HM (D, D.1, E), with its focus on OR/MS and cross-disciplinary research, are particularly
well-placed in driving innovation in the theory and practice of M&S. Application of these innovative HM
methodologies will lead to innovation in the application space as new approaches in stakeholder
engagement, conceptual modelling, system representation, V&V, experimentation, etc. are identified
The Significance of Machine Learning in Clinical Disease Diagnosis: A Review
The global need for effective disease diagnosis remains substantial, given
the complexities of various disease mechanisms and diverse patient symptoms. To
tackle these challenges, researchers, physicians, and patients are turning to
machine learning (ML), an artificial intelligence (AI) discipline, to develop
solutions. By leveraging sophisticated ML and AI methods, healthcare
stakeholders gain enhanced diagnostic and treatment capabilities. However,
there is a scarcity of research focused on ML algorithms for enhancing the
accuracy and computational efficiency. This research investigates the capacity
of machine learning algorithms to improve the transmission of heart rate data
in time series healthcare metrics, concentrating particularly on optimizing
accuracy and efficiency. By exploring various ML algorithms used in healthcare
applications, the review presents the latest trends and approaches in ML-based
disease diagnosis (MLBDD). The factors under consideration include the
algorithm utilized, the types of diseases targeted, the data types employed,
the applications, and the evaluation metrics. This review aims to shed light on
the prospects of ML in healthcare, particularly in disease diagnosis. By
analyzing the current literature, the study provides insights into
state-of-the-art methodologies and their performance metrics.Comment: 8 page
Oxidative Stress in the Pathogenesis of Aorta Diseases as a Source of Potential Biomarkers and Therapeutic Targets, with a Particular Focus on Ascending Aorta Aneurysms
: Aorta diseases, such as ascending aorta aneurysm (AsAA), are complex pathologies, currently defined as inflammatory diseases with a strong genetic susceptibility. They are difficult to manage, being insidious and silent pathologies whose diagnosis is based only on imaging data. No diagnostic and prognostic biomarkers or markers of outcome have been known until now. Thus, their identification is imperative. Certainly, a deep understanding of the mechanisms and pathways involved in their pathogenesis might help in such research. Recently, the key role of oxidative stress (OS) on the pathophysiology of aorta disease has emerged. Here, we describe and discuss these aspects by revealing some OS pathways as potential biomarkers, their underlying limitations, and potential solutions and approaches, as well as some potential treatments
Body Fat Percentage Prediction Using Intelligent Hybrid Approaches
Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models
Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization
In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.).
The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging.
In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place.
We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting
series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf
ECG based Prediction Model for Cardiac-Related Diseases using Machine Learning Techniques
This dissertation presents research on the construction of predictive models for health
conditions through the application of Artificial Intelligence methods. The work is thus
focused on the prediction, in the short and long term, of Atrial Fibrillation conditions
through the analysis of Electrocardiography exams, with the use of several techniques
to reduce noise and interference, as well as their representation through spectrograms
and their application in Artificial Intelligence models, specifically Deep Learning. The
training and testing processes of the models made use of a publicly available database.
In its two approaches, predictive algorithms were obtained with an accuracy of 96.73%
for a short horizon prediction and 96.52% for long Atrial Fibrillation prediction
horizon. The main objectives of this dissertation are thus the study of works already
carried out in the area during the last decade, to present a new methodology of
prediction of the presented condition, as well as to present and discuss its results,
including suggestions for improvement for future development.Esta dissertação descreve a construção de modelos preditivos de condições de saúde
através de aplicação de métodos de Inteligência Artificial. O trabalho é assim focado na
predição, a curto e longo prazo, de condições de Fibrilhação Auricular através da
análise de exames de Eletrocardiografia, com a utilização de diversas técnicas de
redução de ruído e de interferência, bem como a sua representação através de
espectrogramas e sua aplicação em modelos de Inteligência Artificial, concretamente de
Aprendizagem Profunda (Deep Learning na língua inglesa). Os processos de treino e
teste dos modelos obtidos recorreram a uma base de dados publicamente disponível.
Nas suas duas abordagens, foram obtidos algoritmos preditivos com uma precisão de
96.73% para uma predição de curto horizonte e 96.52% para longo horizonte de
predição de Fibrilhação Auricular. Os objetivos principais da presente dissertação são
assim o estudo de trabalhos já realizados na área durante a última década, apresentar
uma nova metodologia de predição da condição apresentada, bem como apresentar e
discutir os seus resultados, incluindo sugestões de melhoria para futuro
desenvolvimento
Focal Spot, Spring 2006
https://digitalcommons.wustl.edu/focal_spot_archives/1102/thumbnail.jp
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