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1 |
IoT architecture for continuous long term monitoring: Parkinson’s Disease case study
معماری اینترنت اشیا برای نظارت طولانی مدت مداوم: مطالعه موردی بیماری پارکینسون-2022 In recent years, technological advancements and the strengthening of the Internet of Things
concepts have led to significant improvements in the technology infrastructures for remote
monitoring. This includes telemedicine which is the ensemble of technologies and tools involved
in medical services, from consultations, to diagnosis, prescriptions, treatment and patient
monitoring, all done remotely via an Internet connection.
Developing a telemedicine framework capable of monitoring patients over a continuous long-term monitoring window may encounter various issues related to the battery life of the device or the accuracy of the retrieved data. Moreover, it is crucial to develop an IoT architecture that is adaptable to various scenarios and the ongoing changes of the application scenario under analysis. In this work, we present an IoT architecture for continuous long-term monitoring of patients. Furthermore, as a real scenario case study, we adapt our IoT architecture for Parkinson’s Disease management, building up the PDRMA (Parkinson’s disease remote monitoring architecture). Performance analysis for optimal operation with respect to temperature and daily battery life is conducted. Finally, a multi-parameter app for the continuous monitoring of Parkinson’s patients is presented. keywords: IoT | Telemedicine | Continuous long term monitoring | Parkinson’s disease | e-Health |
مقاله انگلیسی |
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Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision
Cov-Net: یک روش تشخیصی به کمک رایانه برای تشخیص COVID-19 از تصاویر اشعه ایکس قفسه سینه از طریق بینایی ماشین-2022 In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human
beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this
paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19
from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust
feature learning ability. In particular, a modified residual network with asymmetric convolution and attention
mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated
convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic
and low-level detailed information. Experimental results on two public COVID-19 radiography databases have
demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966
and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms
other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of
Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed
that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios.
Consequently, one can conclude that this work has both practical value in providing reliable reference to the
radiologist and theoretical significance in developing methods to build robust features with strong presentation
ability.
keywords: COVID-19 | Computer aided diagnosis (CAD) | Feature learning | Image recognition | Machine vision |
مقاله انگلیسی |
3 |
Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022 Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors
depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and
treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning
such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for
classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the
synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the
Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human
reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future
many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of
tumors. keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron |
مقاله انگلیسی |
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HealthCloud: A system for monitoring health status of heart patients using machine learning and cloud computing
HealthCloud: سیستمی برای نظارت بر وضعیت سلامت بیماران قلبی با استفاده از یادگیری ماشین و محاسبات ابری-2022 In the context of the global health crisis of 2020, the tendency of many people to self-diagnose at
home virtually, prior to any physical interaction with medical professionals, has been increased.
Existing self-diagnosis systems include those accessible via the Internet, which involve entering
one’s symptoms. Several other methods do exist, for example, people read medical blogs or
notes, which are often wrongly interpreted by them and they arrive at a completely different
assumption regarding the cause of their symptoms. In this paper, a system called HealthCloud
is proposed, for monitoring health status of heart patients using machine learning and cloud
computing. This study aims to offer the ‘best of both worlds’, by combining the information
required for the person to understand the disease in sufficient detail, with an accurate prediction
as to whether they may have (in this case) heart disease or not. The presence of heart disease
is predicted using machine learning algorithms such as Support Vector Machine, K-Nearest
Neighbours, Neural Networks, Logistic Regression and Gradient Boosting Trees. This paper
evaluates these machine learning algorithms to obtain the most accurate model, in compliance
with Quality of Service (QoS) parameters. The performance of these machine learning models
is measured and compared using the metrics such as Accuracy, Sensitivity (Recall), Specificity,
AUC scores, Execution Time, Latency, and Memory Usage. For better establishment of the
results, these machine learning algorithms have been cross validated with 5-fold cross validation
technique. With an accuracy rate of 85.96%, it has been found that Logistic Regression is the
most responsive and accurate model amongst those models assessed. The Precision, Recall,
Cross Validation mean and AUC Score for this model were 95.83%, 76.67%, 81.68% and 96%
respectively. The algorithm and the mobile application were tested on Google Cloud Firebase
with existing user inputs from the dataset, as well as with unseen new data. The use of this
system can assist patients, both in reaching self-diagnosis decisions and in monitoring their
health.
keywords: Machine learning | Smart healthcare | Heart disease prediction | Cloud computing |
مقاله انگلیسی |
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تشخیص BECT اسپایک براساس ویژگیهای توالی EEG Novel و الگوریتم های LSTM
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 35 صرع خوشخیم با امواج spinous در منطقه زمانی ۲ ( BECT ) یکی از شایعترین syndromes مبتلا به صرع در کودکان است که به طور جدی رشد سیستم برای کودکان را تهدید میکند . مشخصترین ویژگی ۵ BECT وجود تعداد زیادی از electroencephalogram ۶ ( EEG ) در ناحیه Rolandic در طول دوره interictal است که یک اساس مهم برای کمک به neurologists در BECT diag8 است . با توجه به این مساله , این مقاله یک الگوریتم تشخیص BECT spike را براساس توالی زمانی سری زمانی EEG ثبت میکند و حافظه کوتاهمدت حافظه بلند مدت ( LSTM ) را نشان میدهد . سه ویژگی متوالی دامنه زمانی , که به وضوح ۱۲ را مشخص میکنند , برای نمایش EEG استخراج میشوند . ۱۳ تکنیک نمونهگیری اقلیت ترکیبی ( smote ) برای ۱۴ سخنرانی در مورد مساله عدم تعادل در EEGs بکار گرفته میشود و ۱۵ - ( BiLSTM ) برای تشخیص سیخ آموزشدیده است . این الگوریتم با استفاده از دادههای EEG ۱۵ BECT ثبتشده از ۱۷ بیمار Hospital ثبتشده از ۱۷ بیمارستان کودکان , دانشکده پزشکی University ۱۸ ( CHZU ) , مورد ارزیابی قرار میگیرد . این آزمایش نشان میدهد که الگوریتم پیشنهادی میتواند به طور متوسط 88.54 % F [ 1] , ۹۲.۰۴ درصد حساسیت , و ۲۰ 85.75 درصد را بدست آورد , که به طور کلی از چندین روش تشخیص استاندارد ویژگی استفاده میکند .
عبارات راهنما: BECT | تشخیص اسپایک | حوزه زمان EEG ویژگی توالی | مدل LSTM
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مقاله ترجمه شده |
6 |
Diagnosis and management of premature ovarian insufficiency
تشخیص و مدیریت نارسایی تخمدان زودرس-2021 Premature ovarian insufficiency (POI) is a complex clinical syndrome with life-changing physiological and psychological consequence in young women of reproductive age. However, the
understanding of the etiology, diagnosis and optimal intervention
strategies for this condition remains poorly understood. In recent
years advances in epidemiologic and genetic research has
improved our knowledge and awareness of POI. Further prospective randomised trials are required to improve the psychological
and sexual health, fertility treatment options and long-term
management of the impact on bone, cardiovascular and cognitive
impact in women with POI. In this paper we aim to provide an
overview on the diagnosis and management of POI, discuss the
current understanding of the condition and future directions.
keywords: تخریب تخمدان زودرس | درمان جایگزینی هورمون | استروژن | بیماری قلب و عروقی | سلامت شناختی | سلامت باروری | premature ovarian insufficiency | hormone replacement therapy | estrogen-deficiency | cardiovascular disease | cognitive health | reproductive health |
مقاله انگلیسی |
7 |
Midwives knowledge of pre-eclampsia management: A scoping review
دانش ماماها از مدیریت پیشکلامپسی: بررسی اسکاپ-2021 Background: Pre-eclampsia is a multi-organ disease affecting pregnant women from the second trimester
onwards resulting in multiple adverse outcomes. Sub-optimal treatment of pre-eclampsia is linked with
unfavorable outcomes. It is critical for midwives as primary providers to be competent in the diagnosis
and management of pre-eclampsia especially in low-and middle-income countries.
Aim: To identify what midwives’ around the world know about pre-eclampsia management. Methods: A scoping review using the JBI three-step search strategy was used to identify relevant research articles and grey literature on the subject. Database searches in PubMed, CINAHL, Cochrane Databases, Web of Science, and Scopus yielded twenty papers in addition to nine guidelines from Google Scholar. The findings were synthesised using a metasynthesis approach and presented as themes. Findings: Four themes were identified from the extracted data: Foundational knowledge of preeclampsia; Knowledge and management of a woman with pre-eclampsia according to guidelines; Knowledge of being prepared for emergency procedures and management of emergencies; Factors influencing knowledge. The first three themes addressed diagnosis and management whilst the last theme described how contextual factors led to either increased or decreased knowledge of preeclampsia. Conclusion: Worldwide, practicing midwives lack knowledge on several aspects of pre-eclampsia diagnosis and care. Policies on in-service training should be oriented to include innovative nontraditional methods that have the potential to increase midwives’ knowledge. keywords: ماماها | دانش | اطلاع | پیش از اکلامپسی | اختلالات فشار خون بالا بارداری | Midwives | Knowledge | Awareness | Pre-eclampsia | Hypertensive disorders pregnancy |
مقاله انگلیسی |
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Assessment of Medical Expenditure for Patients With Breast Cancer in China: Evidence From Current Curative Expenditure by System of Health Accounts 2011
ارزیابی هزینه های پزشکی برای بیماران مبتلا به سرطان پستان در چین: شواهد از هزینه های درمانی فعلی توسط سیستم حساب های بهداشتی 2011-2021 Objectives: The incidence and mortality of breast cancer have been increasing in China and bring heavy economic burdens to
patients, families, and society. This study aimed to analyze the structure and influencing factors of inpatient expenditures of
patients with breast cancer and put forward suggestions for insurance management.
Methods: A multistage stratified random sampling method was used to investigate 379 medical institutions and 7366 pieces of inpatient records of patients with breast cancer in Dalian in 2018. Under the framework of “System of Health Accounts 2011,” the current curative expenditure (CCE) and its distribution were calculated. The relationships between hospitalization expenditure and factors were analyzed by multiple stepwise regression and structural equation modeling. Results: The CCE of patients with breast cancer in Dalian in 2018 was U273.38 million, accounting for 10.66% of the total expenditure on cancer. The majority of the CCE flowed to large general hospitals. The CCE was concentrated in patients aged 40 to 69 years (23.46%). The hospitalization expenditure correlated positively with length of stay, surgery, and drug expenses (rs = 0.586-0.754, P,.01) and negatively associated with age (rs = 20.074, P,.01). The length of stay mediated the relationship between surgery and hospitalization expenses for patients with breast cancer. The factors that affected the hospitalization expenditure were the drug expenses, surgery, length of stay, insurance status, and institution level. Conclusions: The cost control for CCE of breast cancer inpatient treatment is crucial in China. Promoting hierarchical diagnosis and treatment, reducing the length of stay, and improving medical insurance depth would be effective measures to reduce the financial burden of patients. keywords: breast cancer | current curative expenditure | hospitalization expenses | System of Health Accounts 2011 |
مقاله انگلیسی |
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شیوع، همبستگیهای اجتماعی-جمعیتی و دانشگاهی اختلال وسواسی جبری در دانشجویان دانشکده علوم پزشکی کاربردی دانشگاه ام القرا
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 21 مقدمه: مطالعاتی که شیوع وسواس جبری را در منطقه عربستان سعودی نشان میدهد بسیار اندک است و بیشتر در نمونه جمعیتی دانشجویان پزشکی و پیراپزشکی وجود دارد. هدف از این مطالعه برآورد شیوع علائم وسواس اجباری در یک نمونه جامعه دانشجویان علوم پزشکی کاربردی بود. علاوه بر این، ارتباط بین علائم وسواسی جبری و متغیرهای اجتماعی-جمعیتی و چندین جنبه از زندگی دانشگاهی بررسی شد.
روشها: در این مطالعه مقطعی 404 دانشجوی دانشگاه متعلق به چهار بخش به کار گرفته شدند. ابزارهایی که در این مطالعه استفاده شد، شامل معیارهای ارزیابی وسواس جبری (OCI - R) ، DSM - IV برای تشخیص مقیاس درجه بندی شدت OCD و Y - BOCS بود. نتیجه اصلی اختلال وسواس جبری احتمالی است (امتیاز OCI - R> 21). دانشجویان با نمره بیشتر از 21 بیشتر از نظر وجود اختلال وسواس جبری با استفاده از معیارهای DSM - IV و Y - BOCS ارزیابی شدند. یافته ها: شیوع OCS با ابزار غربالگری OCI-R 20% بود [95% CI(19.902-20.098)]. شیوع واقعی OCD تأیید شده 5.06٪ بود [95% CI(4.39-6.12)]. وجود OCD احتمالی در دانشجویان گروه آزمایشگاه پزشکی بسیار زیاد بود [002/0 = p و95% CI(31.3-3.33) [. ارتباط مهمی بین حضور OCS و عدم رضایت از انتخاب دوره [001/0 = p ، 95٪ CI (1.38 - 3.92)] ، احساس طرد شدن [0.004 = p ، 95٪ CI (1.39 - 5.88]) و علائم افسردگی [0001/0 = p و CI (8/1 - 89/1)] وجود داشت. نمونه ما به زنان در سن دانشگاه محدود بود، بنابراین تفسیر شیوع قابل تعمیم نیست. نتیجه گیری: وجود چنین اختلالی احتمالاً بر عملکرد تحصیلی ، کیفیت زندگی و روابط بین فردی تأثیر می گذارد ، شناسایی و درمان در زمان مناسب به بهبود عملکرد تحصیلی و کیفیت زندگی کمک می کند. کلمات کلیدی: وسواس جبری | علائم وسواسی جبری | دانشجویان پزشکی و پیراپزشکی | اختلال روانی |
مقاله ترجمه شده |
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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 |