97 research outputs found
Weighted bee colony algorithm for discrete optimization problems with application to feature selection
Modeling Suspicious Email Detection using Enhanced Feature Selection
The paper presents a suspicious email detection model which incorporates
enhanced feature selection. In the paper we proposed the use of feature
selection strategies along with classification technique for terrorists email
detection. The presented model focuses on the evaluation of machine learning
algorithms such as decision tree (ID3), logistic regression, Na\"ive Bayes
(NB), and Support Vector Machine (SVM) for detecting emails containing
suspicious content. In the literature, various algorithms achieved good
accuracy for the desired task. However, the results achieved by those
algorithms can be further improved by using appropriate feature selection
mechanisms. We have identified the use of a specific feature selection scheme
that improves the performance of the existing algorithms
"If only had I known":a qualitative study investigating a treatment of patients with a hip fracture with short time stay in hospital
Hip fractures are amongst the leading causes of admission to an orthopaedic ward. Systematized pathways with reduced admission time have become increasingly common as an essential tool for quality development and to improve efficiency in the hospital setting.  The aim of this study was to clarify if the patients feel empowered and able to perform self-care after short time stay in hospital (STSH) due to a hip fracture. The study used descriptive phenomenology to describe experiences of the pathway. Field studies were conducted in hospitals and in the patients' homes.  Interviews were performed with 10 patients recruited from two wards at a Danish University hospital, 4 family members and 15 health professionals from three hospitals.  The open attitude of reflective lifeworld research guided the analysis. The findings revealed that patients felt unprepared and insecure about their future, but also had a strong desire to be in charge of their own lives.  Of all the patients interviewed, none had any recollection of the information given to them by health professionals during their hospital admission. This study demonstrates that empowerment of patients with hip fractures is not adequately achieved in the pathway with STSH
Monkeypox detection using deep neural networks
BACKGROUND: In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around the world. The experience with Coronavirus Disease 2019 (COVID-19) has increased awareness about pandemics among researchers and health authorities.METHODS: Deep Neural Networks (DNNs) have shown promising performance in detecting COVID-19 and predicting its outcomes. As a result, researchers have begun applying similar methods to detect Monkeypox disease. In this study, we utilize a dataset comprising skin images of three diseases: Monkeypox, Chickenpox, Measles, and Normal cases. We develop seven DNN models to identify Monkeypox from these images. Two scenarios of including two classes and four classes are implemented.RESULTS: The results show that our proposed DenseNet201-based architecture has the best performance, with Accuracy = 97.63%, F1-Score = 90.51%, and Area Under Curve (AUC) = 94.27% in two-class scenario; and Accuracy = 95.18%, F1-Score = 89.61%, AUC = 92.06% for four-class scenario. Comparing our study with previous studies with similar scenarios, shows that our proposed model demonstrates superior performance, particularly in terms of the F1-Score metric. For the sake of transparency and explainability, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-Cam) were developed to interpret the results. These techniques aim to provide insights into the decision-making process, thereby increasing the trust of clinicians.CONCLUSION: The DenseNet201 model outperforms the other models in terms of the confusion metrics, regardless of the scenario. One significant accomplishment of this study is the utilization of LIME and Grad-Cam to identify the affected areas and assess their significance in diagnosing diseases based on skin images. By incorporating these techniques, we enhance our understanding of the infected regions and their relevance in distinguishing Monkeypox from other similar diseases. Our proposed model can serve as a valuable auxiliary tool for diagnosing Monkeypox and distinguishing it from other related conditions.</p
Efficacy of a web-based healthcare innovation to advance the quality of life and care of patients with an implantable cardioverter defibrillator (ACQUIRE-ICD): a randomized controlled trial
AIMS: Modern clinical management of patients with an implantable cardioverter defibrillator (ICD) largely consists of remote device monitoring, although a subset is at risk of mental health issues post-implantation. We compared a 12-month web-based intervention consisting of goal setting, monitoring of patients' mental health-with a psychological intervention if needed-psychoeducational support from a nurse, and an online patient forum, with usual care on participants' device acceptance 12 months after implantation.METHODS AND RESULTS: This national, multi-site, two-arm, non-blinded, randomized, controlled, superiority trial enrolled 478 first-time ICD recipients from all 6 implantation centres in Denmark. The primary endpoint was patient device acceptance measured by the Florida Patient Acceptance Survey (FPAS; general score range = 0-100, with higher scores indicating higher device acceptance) 12 months after implantation. Secondary endpoints included symptoms of depression and anxiety. The primary endpoint of device acceptance was not different between groups at 12 months [B = -2.67, 95% confidence interval (CI) (-5.62, 0.29), P = 0.08]. Furthermore, the secondary endpoint analyses showed no significant treatment effect on either depressive [B = -0.49, 95% CI (-1.19; 0.21), P = 0.17] or anxiety symptoms [B = -0.39, 95% CI (-0.96; 0.18), P = 0.18].CONCLUSION: The web-based intervention as supplement to usual care did not improve patient device acceptance nor symptoms of anxiety and depression compared with usual care. This specific web-based intervention thus cannot be recommended as a standardized intervention in ICD patients.</p
Patient@home Ecosystem
Abstract
Patient@home was the largest Danish project within health and
welfare technology. The aim was to support the health and care sector
with innovative technologies to meet the increasing demands of an
aging and more chronically ill population. The underlying hypothesis
was that the increased use of technology could help minimize hospital
admissions in both number and duration. New technologies enable
carefully selected groups of patients to stay longer in their own
homes to the benefit of both patients (and their relatives) and
the health and care sectors. A unique ecosystem for the development,
assessment, and testing of health and welfare technology was developed,
refined and matured from 2012 to 2018. The ecosystem covered the
entire value chain from research to implementation; it had a broad
national coverage and an international outreach with more than 100
partners (including 66 private companies). The ecosystem provided
a set of services that supported knowledge and evidence based development
and assessment. Numerous contributing partners were aligned to each
focus on what they do best. A “best practice” method for successful
product and service development was established based on examination of
successful projects in the portfolio. The ecosystem deployed specialized
methods for technology assessment and smart innovation management
Issues in the Design of EHTS: A Multiuser Hypertext System for Collaboration
This paper describes the architecture, issues from the design, and experiences from the use of EHTS (Emacs HyperText System), a multiuser hypertext system for collaboration. EHTS consists of a text editor, a graphical browser, and an active hypertext database named HyperBase. HyperBase is built on the client-server model and has been designed especially to support collaboration among its users, by providing an event mechanism and a fine-grained lock mechanism. Events from HyperBase are used to notify the editor and browser about important actions on the shared data, enabling them to monitor changes. Four categories of issues from the design and experiences from the use of EHTS are reported: architecture, collaboration, user interface, and data model. Based on our experiences with the client-server model, we suggest a new and improved architecture for the event driven multiuser hypertext system. One major lesson learned is that events and fine-grained locks can provide powerful support for data sharing among multiple users simultaneously working in the same environment. The need for a flexible data model is another lesson learned. It is difficult to predict what data model objects actually will be needed, when the data model is designed before the user interface
Workspaces: The HyperDisco Approach to Internet Distribution
Hypermedia concepts are currently being deployed in a variety of information systems such as the World Wide Web, software development environments, large engineering enterprises, collaborative authoring systems, and digital library systems. The complex requirements of these application areas have resulted in extensive research into hypermedia infrastructures. The HyperDisco project is about design, development, deployment and assessment of hypermedia infrastructures. Previous HyperDisco experiments have dealt with integration of a small set of tools supporting authoring and extension of the integrated tools to support multiple collaborating users and multiple versions of shared files. These experiments were conducted on a local area network using a single centralized workspace. The latest version of HyperDisco supports collaboration and versioning over multiple workspaces distributed across the Internet. This paper gives a brief overview of HyperDisco, describes the workspace concept and reports on the latest experiments: (1) an experiment that allows the use of multiple workspaces on a local area network, (2) an experiment that allows workspaces to be distributed across the Internet, and (3) an experiment focusing on hypermedia modeling and presentation issues of distributed workspaces
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