Search results for "learning."
showing 10 items of 6527 documents
From laparoscopic assisted radical vaginal hysterectomy to vaginal assisted laparoscopic radical hysterectomy
2011
Radical hysterectomy with pelvic lymphadenectomy is the standard surgical treatment for patients with early stage cervical cancer. The majority of radical hysterectomies are performed with the open technique. However, laparoscopic, combined laparoscopic and vaginal, and robotic-assisted approaches may also be used. Compared with the abdominal radical hysterectomy (ARH), laparoscopic techniques are associated with less blood loss, shorter hospital stay, better cosmesis, and faster recovery. A further breakthrough in laparoscopic technique can only be made if safety and oncological clearance are comparable with ARH. We describe the technique and results of laparoscopic assisted radical vagina…
Instructions for External Focus of Attention Improved Taekwondo Kicking Performance Only Among Less Skilled Youth
2022
External focus of attention (EFA) studies among children have yielded more equivocal results than have those among adults. Some investigators have found an internal focus of attention (IFA) advantage in children and have explained their results by children’s generally lower skill levels, compared to adults. According to the constrained action hypothesis, children’s lower skill levels are not yet associated with over-learned automatic movement patterns, so their motor performance is not disrupted by IFA instructions. In this study, our objective was to examine a possible interaction effect between children’s skill levels and their exposure to either IFA or EFA instructions on motor performa…
How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as …
2021
Background The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as medical devices (MD), being important to assess the associated risks. Methods An anemia control model (ACM), certified as MD may face adverse events as the result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard. While severities are typically assessed by clinicians, probabilities are tightly related to the performance of the predictive model. Results A post-marketing dataset formed by all adult patients registe…
Achievement of treatment targets predicts progression of vascular complications in type 1 diabetes.
2021
Abstract Background and aim To study the association between achievement of guideline-defined treatment targets on HbA1c, low-density lipoproteins (LDL-C), and blood pressure with the progression of diabetic complications in patients with type 1 diabetes (T1D). Methods The study included 355 patients at baseline and 114 patients with follow-up data after 3–5 years. Outcome variables were the progression of diabetic kidney disease, retinopathy, or cardiovascular disease (CVD). We used logistic regression and other machine learning algorithms (MLA) to model the association of achievement of treatment targets and probability of progression of complications. Results Achievement of the target bl…
Evaluation of saliva as a complementary technique to the diagnosis of COVID-19:a systematic review
2021
Background Infectious disease coronavirus 2019 (COVID-19) is caused by the SARS-CoV-2 virus, and it mainly affects the upper respiratory tract. The gold standard for its diagnosis is real-time reverse transcription polymerase chain reaction (RT-qPCR) performed on a nasopharyngeal swab. In contrast, testing saliva has significant advantages as a diagnostic method. Material and Methods We searched for articles evaluating saliva as a diagnostic method for COVID-19 on the PUBMED/MEDLINE, WEB OF SCIENCE, COCHRANE, and SCIELO platforms. We initially found 233 articles and 20 were selected for inclusion following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol: 18 c…
Learning Curve and Fatigue Effect of Flicker Defined Form Perimetry
2011
To evaluate the learning curve and fatigue effect of flicker defined form (FDF) perimetry.Prospective cross-sectional study.One hundred forty-one eyes of 75 healthy subjects were included in the study. Every subject was measured 3 times on 3 different days within 3 months. Differences among the tests were analyzed for mean sensitivity (MS), mean deviation (MD), pattern standard deviation (PSD), reliability indices, test duration, and test points5% and0.5% in 75 right eyes on the basis of linear mixed models for repeated measurements. To assess the effect of fatigue, differences of MS, MD, and PSD values between 66 left and right eyes were evaluated regarding alterations between these 3 exam…
Continued Multidisciplinary Project-based Learning – Implementation in Health Informatics
2008
Summary Objectives: Problem- and project-based learning are approved methods to train students, graduates and post-graduates in scientific and other professional skills. The students are trained on realistic scenarios in a broader context. For students specializing in health informatics we introduced continued multidisciplinary project-based learning (CM-PBL) at a department of medical informatics. The training approach addresses both students of medicine and students of computer science. Methods: The students are full members of an ongoing research project and develop a project-related application or module, or explore or evaluate a sub-project. Two teachers guide and review the students’ …
A Novel Deep Learning Stack for APT Detection
2019
We present a novel Deep Learning (DL) stack for detecting Advanced Persistent threat (APT) attacks. This model is based on a theoretical approach where an APT is observed as a multi-vector multi-stage attack with a continuous strategic campaign. To capture these attacks, the entire network flow and particularly raw data must be used as an input for the detection process. By combining different types of tailored DL-methods, it is possible to capture certain types of anomalies and behaviour. Our method essentially breaks down a bigger problem into smaller tasks, tries to solve these sequentially and finally returns a conclusive result. This concept paper outlines, for example, the problems an…
State of the Art Literature Review on Network Anomaly Detection with Deep Learning
2018
As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additi…
Thompson Sampling Guided Stochastic Searching on the Line for Non-stationary Adversarial Learning
2015
This paper reports the first known solution to the N-Door puzzle when the environment is both non-stationary and deceptive (adversarial learning). The Multi-Armed-Bandit (MAB) problem is the iconic representation of the exploration versus exploitation dilemma. In brief, a gambler repeatedly selects and play, one out of N possible slot machines or arms and either receives a reward or a penalty. The objective of the gambler is then to locate the most rewarding arm to play, while in the process maximize his winnings. In this paper we investigate a challenging variant of the MAB problem, namely the non-stationary N-Door puzzle. Here, instead of directly observing the reward, the gambler is only…