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نتیجه جستجو - Personalized

تعداد مقالات یافته شده: 116
ردیف عنوان نوع
1 Knowledge of healthcare providers in the management of anaphylaxis
آگاهی از ارائه دهندگان خدمات بهداشتی در مدیریت آنافیلاکسی-2021
Introduction: Anaphylaxis is defined as a severe, life-threatening systemic hypersensitivity reaction. Early diagnosis and treatment of a severe allergic reaction requires recognition of the signs and symptoms, as well as classification of severity. It is a clinical emergency, and healthcare providers should have the knowledge for recognition and management. The aim of the study is to evaluate the level of knowledge in the management of anaphylaxis in healthcare providers.
Methods: It is an observational, descriptive, cross-sectional study conducted among healthcare providers over 18 years old via a Google Forms link and shared through different social media platforms. A 12-item questionnaire was applied which included the evaluation of the management of anaphylaxis, from June 2020 to May 2021.
Results: A total of 1023 surveys were evaluated; 1013 met inclusion criteria and were included in the statistical analysis. A passing grade was considered with 8 or more correct answers out of 12; the overall approval percentage was 28.7%. The group with the highest percentage of approval in the questionnaire was health-care providers with more than 30 years of work experience. There was a significant difference between the proportions of approval between all specialty groups, and in a post-hoc analysis, allergy and immunology specialists showed greater proportions of approval compared to general medicine practitioners (62.9% vs 25%; p¼<0.001).
Conclusions: It is important that healthcare providers know how to recognize, diagnose, and treat anaphylaxis, and later refer them to specialists in Allergy and Clinical Immunology in order to make a personalized diagnosis and treatment.
Keywords: Anaphylaxis | Epinephrine | Healthcare providers | Knowledge
مقاله انگلیسی
2 Drawing the premises for personalized learning: Illustrations of management and accounting
طراحی مکان برای یادگیری شخصی: تصاویر مدیریت و حسابداری-2021
This paper reports on a teaching innovation using participant-generated drawings. Experienced managers were asked to produce a drawing to illustrate their work from an accounting perspective. The drawings were then used to make the managerial context of the participants the explicit starting point for personalized executive learning. This study is the first in the sphere of accounting education and research to take drawing seriously as a learning method. The results of the experiment show how drawing can be further used as a tool in management education by facilitating the visualization of the managerial contexts participants work within.
keywords: حسابداری | تحصیلات | نقاشی کردن | آموزش حسابداری | کار مدیریتی | حسابداری مدیریت | روش های بصری | Accounting | Education | Drawing | Accounting education | Managerial work | Management accounting | Visual methods
مقاله انگلیسی
3 AI-based Reference Ankle Joint Torque Trajectory Generation for Robotic Gait Assistance: First Steps
تولید مسیر حرکت گشتاور مفصل مچ پا مبتنی بر هوش مصنوعی برای کمک به راه رفتن رباتیک: اولین قدم ها-2020
Robotic-based gait rehabilitation and assistance have been growing to augment and to recover motor function in subjects with lower limb impairments. There is interest in developing user-oriented control strategies to provide personalized assistance. However, it is still needed to set the healthy user-oriented reference joint trajectories, namely, reference ankle joint torque, that would be desired under healthy conditions. Considering the potential of Artificial Intelligence (AI) algorithms to model nonlinear relationships of the walking motion, this study implements and compares two offline AI-based regression models (Multilayer Perceptron and Long-Short Term Memory-LSTM) to generate healthy reference ankle joint torques oriented to subjects with a body height ranging from 1.51 to 1.83 m, body mass from 52.0 to 83.7 kg and walking in a flat surface with a walking speed from 1.0 to 4.0 km/h. The best results were achieved for the LSTM, reaching a Goodness of Fit and a Normalized Root Mean Square Error of 79.6 % and 4.31 %, respectively. The findings showed that the implemented LSTM has the potential to be integrated into control architectures of robotic assistive devices to accurately estimate healthy useroriented reference ankle joint torque trajectories, which are needed in personalized and Assist-As-Needed conditions. Future challenges involve the exploration of other regression models and the reference torque prediction for remaining lower limb joints, considering a wider range of body masses, heights, walking speeds, and locomotion modes.
Keywords: Ankle Joint Torque Prediction | Artificial Intelligence | Control Strategies | Regression Models | Robotic Gait Rehabilitation
مقاله انگلیسی
4 Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine
یکپارچه سازی هوش مصنوعی و داروی آزمایشگاهی در پزشکی دقیق قلب و عروق-2020
Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enable cost-effectiveness with reduce readmission and mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.
Keywords: Artificial intelligence | Cardiology | Laboratory | Biomarkers | Data | Machine learning | Personalized
مقاله انگلیسی
5 An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
مقاله انگلیسی
6 Towards Security and Privacy for Edge AI in IoT/IoE based Digital Marketing Environments
به سمت امنیت و حفظ حریم خصوصی برای هوش مصنوعی لبه در محیط های بازاریابی دیجیتال مبتنی بر IoT / IoE-2020
Abstract—Edge Artificial Intelligence (Edge AI) is a crucial aspect of the current and futuristic digital marketing Internet of Things (IoT) / Internet of Everything (IoE) environment. Consumers often provide data to marketers which is used to enhance services and provide a personalized customer experience (CX). However, use, storage and processing of data has been a key concern. Edge computing can enhance security and privacy which has been said to raise the current state of the art in these areas. For example, when certain processing of data can be done local to where requested, security and privacy can be enhanced. However, Edge AI in such an environment can be prone to its own security and privacy considerations, especially in the digital marketing context where personal data is involved. An ongoing challenge is maintaining security in such context and meeting various legal privacy requirements as they themselves continue to evolve, and many of which are not entirely clear from the technical perspective. This paper navigates some key security and privacy issues for Edge AI in IoT/IoE digital marketing environments along with some possible mitigations.
Keywords: edge security | edge privacy | edge AI | edge intelligence | artificial intelligence | AI | machine learning | ML | IoT | IoE | edge | cybersecurity | legal | law | digital marketing | smart | GDPR | CCPA | security | privacy
مقاله انگلیسی
7 Reinforcement learning application in diabetes blood glucose control : A systematic review
کاربرد یادگیری تقویتی در کنترل قند خون دیابت : یک بررسی سیستماتیک-2020
Background: Reinforcement learning (RL) is a computational approach to understanding and automating goaldirected learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient’s own data. Objective: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support and personalized feedback in the context of DM. Methods: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection. Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions: The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms.
Keywords: Reinforcement learning | Blood glucose control | Artificial pancreas | Closed-loop | Insulin infusion
مقاله انگلیسی
8 Supply and Demand of Ai-Aided Language Education
تأمین و تقاضای آموزش زبان به کمک هوش مصنوعی-2020
Intelligent technology empowers foreign language education, boosting the development of various kinds of learning APPS and websites. Artificial intelligence -assisted foreign language education can balance education supply and demand, meanwhile it can reduce education costs, bringing more benefits to education participants. Furthermore, it will improve learning efficiency, and promote education equity. Big data, learning and analysis technology can offer learners individualized content, personalized guidance and diagnostic evaluation, increasing education supply of learning materials, tutors and learning venues.
Keywords: Artificial Intelligence | language education | supply and demand
مقاله انگلیسی
9 Therapy-driven Deep Glucose Forecasting
پیش بینی گلوکز عمیق درمان محور-2020
The automatic regulation of blood glucose for Type 1 diabetes patients is the main goal of the artificial pancreas, a closed-loop system that exploits continue glucose monitoring data to define an optimal insulin therapy. One of the most successful approaches for developing the artificial pancreas is the model predictive control, which exhibits promising results on both virtual and real patients. The performance of such controller is highly dependent on the reliability of the glucose–insulin model used for prediction purpose, which is usually implemented with classic mathematical models. The main limitation of these models consists in the difficulties of modeling the physiological nonlinear dynamics typical of this system. The availability of big amount of in silico and in vivo data moved the attention to new data-driven methods which are able to easily overcome this problem. In this paper we propose Deep Glucose Forecasting, a deep learning approach for forecasting glucose levels, based on a novel, two-headed Long-Short Term Memory implementation. It takes in input the previous values obtained through continue glucose monitoring, the carbohydrate intake, the suggested insulin therapy and forecasts the interstitial glucose level of the patient. The proposed architecture has been trained on 100 virtual adult patients of the UVA/Padova simulator, and tested on both virtual and real patients. The proposed solution is able to generalize to new unseen data, outperforms classical population models and reaches performance comparable to classical personalized models when fine-tuning is exploited on real patients.
Keywords: Diabetes | Forecasting | Prediction | Deep learning | LSTM
مقاله انگلیسی
10 A reinforcement learning model for personalized driving policies identification
یک مدل یادگیری تقویتی برای شناسایی شخصیت های سیاسی محور -2020
Optimizing driving performance by addressing personalized aspects of driving behavior and without posing unrealistic restrictions on personal mobility may have far reaching implications to traffic safety, flow operations and the environment, as well as significant benefits for users. The present work addresses the problem of delivering personalized driving policies based on Reinforcement Learning for enhancing existing Intelligent Transportation Systems (ITS) to the benefit of traffic management and road safety. The proposed framework is implemented on appropriate driving behavior metrics derived from smartphone sensors’ data streams. Aggressiveness, speeding and mobile usage are considered to describe the driving profile per trip and are presented as inputs to the Q-learning algorithm. The implementation of the proposed methodological approach produces personalized quantified driving policies to be exploited for self-improvement. Finally, this paper establishes validation measures of the quality and effectiveness of the produced policies and methodological tools for comparing and classifying the examined drivers.
Keywords: Reinforcement learning | Q-learning | Machine learning | Intelligent transportation systems | Traffic data
مقاله انگلیسی
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