دانلود و نمایش مقالات مرتبط با Vital sign::صفحه 1
دانلود بهترین مقالات isi همراه با ترجمه فارسی 2

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - Vital sign

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches
یک معماری تعاملی مبتنی بر اینترنت اشیا برای نظارت بی‌سیم بیماران با پچ های حسگر-2022
The alliance between the Internet of Things (IoT) and healthcare has the potential to improve healthcare assistance at different stages of care through distributed vital sign sensing, paving the way for domiciliary hospitalization. In this work, we propose an innovative design for an IoT-based interoperable healthcare system to wirelessly monitor and classify patient status. To support our research, we identify gaps, and discuss standards, protocols and technologies based on works that use relevant IoT applications in healthcare. The proposed architecture is centered on several low-energy unobtrusive sensors attached to the patients’ bodies, as well as their beds, which encompass data acquisition nodes linked to a smart gateway that aggregates data. The smart gateway is integrated with an existing hospital information system through the exchange of Electronic Health Records (EHR), making relevant patient data easily available to health professionals on systems which are familiar to them. A use case scenario is presented in order to fulfill functional and non-functional requirements and provide a better understanding of connection and communication between the distinct entities of the proposed architecture, which is based on Bluetooth Low Energy (BLE) technology at the data acquisition level, the Message Queuing Telemetry Transport (MQTT) protocol at the internal level, and on the Fast Healthcare Interoperability Resources (FHIR) standard at the higher level.
keywords: Internet of Things | Digital healthcare | Wireless patient biomonitoring | System architecture | Interoperability
مقاله انگلیسی
2 Exploring health literacy and self-management after kidney transplantation: A prospective cohort study
بررسی سواد بهداشتی و خود مدیریت پس از پیوند کلیه: یک مطالعه کوهورت آینده نگر-2021
Objective: Investigate the influence of health literacy and self-management on complications, kidney function and graft failure after kidney transplantation. Methods: We included patients who received a kidney transplant between May 2012 and May 2013 and monitored outcomes until December 2018. Health literacy was measured using the Newest Vital Sign and self-management using the Partner in Health scale (before discharge, and after 6 and 12 months). Subscales are aftercare & knowledge, coping, recognition and management of symptoms, healthy lifestyle. Complications were categorized as rejection, viral infections, and bacterial infections. Kidney function was measured using eGFR and graft survival using days until failure. Results: We included 154 patients. Higher health literacy at baseline and at 12 months was related to more viral infections (p = 0.02; p < 0.01). Lower ‘coping’ at baseline was related to more bacterial infections (p = 0.02). Higher ‘after-care and knowledge’ at 6 months (p < 0.01), and ‘recognition and management of symptoms’ at 6 months were associated with lower graft failure (p < 0.01). Conclusion: Health literacy did not influence kidney transplant related outcomes. Higher knowledge and management of symptoms were related to lower graft failure. Practice implications: Self-management support is a key focus for health care providers in the multi- disciplinary team. © 2021 Published by Elsevier B.V.
keywords: سواد بهداشتی | عوامل روان شناختی | پیوند کلیه، عوارض | خود مراقبتی | بقای پیوند | مرحله پایانی بیماری کلیوی | Health literacy | Psychosocial factors | Renal transplantation, complications | Self-care | Graft survival | End-stage renal disease
مقاله انگلیسی
3 Data mining of the best spectral indices for geochemical anomalies of copper: A study in the northwestern Junggar region, Xinjiang
داده کاوی از بهترین شاخص های طیفی برای ناهنجاری های ژئوشیمیایی مس: یک مطالعه در منطقه شمال غربی جونگگر ، سین کیانگ-2019
Hyperspectral remote sensing allows sampling at high temporal resolutions as well as rapid and non-destructive characterization of a wide range of mineralization, enabling identification of element content through spectral features. It provides data for prospecting in areas without sufficient geochemical data, and thus is of vital significance in prospecting for ores in such regions. However, approaches for remotely sensing elements are still lacking, particularly for element content. In this study, a level analysis of Cu content via spectral indices in the northwestern Junggar region, Xinjiang, was conducted. Based on four levels (0–100 ppm, 100–1000 ppm, 1000–10,000 ppm, and>10,000 ppm) of Cu content and corresponding spectral reflectance, simple and useful spectral indices for estimating Cu content at different levels were explored. The best wavelength domains for a given type of index were determined from four types of spectral indices by screening all combinations using correlation analysis. The coefficient of determination (R2) for Cu was calculated for all indices derived from the spectra of rock samples and was found to range from 0.02 to 0.75. With sensitive wavelengths and a significant correlation coefficient (R2=0.63, P < 0.005), the Normalized Difference (ND)-type index was the most sensitive to Cu content exceeding 10,000 ppm. Although the ND-type index has a few limitations, it is a useful, simple, and robust indicator for determining Cu at high concentrations.
Keywords: Cu element | Spectral indices | Geochemical anomaly | Northwestern Junggar region of Xinjiang | Normalized Difference-type index
مقاله انگلیسی
4 Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
ارزیابی یک الگوریتم یادگیری ماشین برای پیش بینی 48 ساعته پیش از سپسیس با استفاده از شش علامت حیاتی-2019
Objective: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods. Materials and methods: Retrospective data was drawn from databases at the University of California, San Francisco (UCSF) Medical Center and the Beth Israel Deaconess Medical Center (BIDMC). Adult patient encounters without sepsis on admission, and with at least one recording of each of six vital signs (SpO2, heart rate, respiratory rate, temperature, systolic and diastolic blood pressure) were included. We compared the performance of the machine learning algorithm (MLA) to that of commonly used scoring systems. Area under the receiver operating characteristic (AUROC) curve was our primary measure of accuracy. MLA performance was measured at sepsis onset, and at 24 and 48 h prior to sepsis onset. Results: The MLA achieved an AUROC of 0.88, 0.84, and 0.83 for sepsis onset and 24 and 48 h prior to onset, respectively. These values were superior to those of SIRS (0.66), MEWS (0.61), SOFA (0.72), and qSOFA (0.60) at time of onset. When trained on UCSF data and tested on BIDMC data, sepsis onset AUROC was 0.89. Discussion and conclusion: The MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.
Keywords: Sepsis | Machine learning | Electronic health records | Prediction
مقاله انگلیسی
5 Survival outcome prediction in cervical cancer: Cox models vs deep-learning model
پیش بینی نتیجه بقا در سرطان دهانه رحم: مدل های کاکس در مقابل مدل یادگیری عمیق-2019
BACKGROUND: Historically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. However, it is likely that, in reality, there are many clinicopathologic features that exhibit a nonlinear association in biomedicine. OBJECTIVE: The purpose of this study was to compare the deeplearning neural network model and the Cox proportional hazard regression model in the prediction of survival in women with cervical cancer. STUDY DESIGN: This was a retrospective pilot study of consecutive cases of newly diagnosed stage IeIV cervical cancer from 2000e2014. A total of 40 features that included patient demographics, vital signs, laboratory test results, tumor characteristics, and treatment types were assessed for analysis and grouped into 3 feature sets. The deeplearning neural network model was compared with the Cox proportional hazard regression model and 3 other survival analysis models for progression-free survival and overall survival. Mean absolute error and concordance index were used to assess the performance of these 5 models. RESULTS: There were 768 women included in the analysis. The median age was 49 years, and the majority were Hispanic (71.7%). The majority of tumors were squamous (75.3%) and stage I (48.7%). The median followup time was 40.2 months; there were 241 events for recurrence and progression and 170 deaths during the follow-up period. The deeplearning model showed promising results in the prediction of progression-free survival when compared with the Cox proportional hazard regression model (mean absolute error, 29.3 vs 316.2). The deep-learning model also outperformed all the other models, including the Cox proportional hazard regression model, for overall survival (mean absolute error, Cox proportional hazard regression vs deep-learning, 43.6 vs 30.7). The performance of the deep-learning model further improved when more features were included (concordance index for progression-free survival: 0.695 for 20 features, 0.787 for 36 features, and 0.795 for 40 features). There were 10 features for progression-free survival and 3 features for overall survival that demonstrated significance only in the deep-learning model, but not in the Cox proportional hazard regression model. There were no features for progression-free survival and 3 features for overall survival that demonstrated significance only in the Cox proportional hazard regression model, but not in the deep-learning model. CONCLUSION: Our study suggests that the deep-learning neural network model may be a useful analytic tool for survival prediction in women with cervical cancer because it exhibited superior performance compared with the Cox proportional hazard regression model. This novel analytic approach may provide clinicians with meaningful survival information that potentially could be integrated into treatment decision-making and planning. Further validation studies are necessary to support this pilot study.
Key words: Cox proportional hazard | cervical cancer | deep learning | survival prediction
مقاله انگلیسی
6 SDDRS: Stacked Discriminative Denoising Auto-Encoder based Recommender System
SDDRS: سیستم توصیه گر مبتنی بر رمزگذار خودکار حذف نویز با استفاده از انکودرهای انکارکننده انباشته شده-2019
Recommender systems are widely used in our life for automatically recommending items relevant to our preference. Collaborative Filtering (CF) is one of the most successful methods in recommendation field. Matrix Factorization (MF) based recommender system is designed according to the basic strategy of the CF algorithm, which is widely adopted recently. However, the rating matrix utilized by these models is usually sparse, so it is of vital significance to integrate the side information to provide relatively effective knowledge for modeling the user or item features. The key problem is to extract effective features from the noisy side information. However, the side information contains a lot of noise except rating knowledge, which makes it a challenging issue for extracting effective features. In this paper, we propose Stacked Discriminative Denoising Auto-Encoder based Recommender System (SDDRS) by integrating deep learning model with MF based recommender system to effectively incorporate side information with rating information. Extensive top-N recommendation experiments conducted on three real-world datasets empirically demonstrate that SDDRS outperforms several state-of-theart methods.
Keywords: Collaborative filtering | Side information | Rating information| Stacked denoising auto-encoder | Matrix factorization
مقاله انگلیسی
7 Development of an In Situ Thoracic Surgery Crisis Simulation Focused on Nontechnical Skill Training
توسعه یک شبیه سازی بحران جراحی توراسیون در منطقه تمرکز بر آموزش مهارت های غیر فنی-2018
Background. Our vision was to develop an inexpensive training simulation in a functional operating room (in situ) that included surgical trainees and nursing and anesthesia staff to focus on effective interprofessional communication and teamwork skills. Methods. The simulation scenario revolved around an airway obstruction by residual tumor after pneumonec tomy. This model included our thoracic operating room with patient status displayed by an open access vital sign simulator and a reversibly modified Laerdal airway mannequin (Shavanger, Norway). The simulation sce nario was run seven times. Simulations were video recorded and scored with the use of Non-Technical Skills for Surgeons (NOTSS) and TeamSTEPPS2. Latent safety threats (LSTs) and feedback were obtained during the debriefing after the simulation. Feedback was captured with the Method Material Member Overall (MMMO) questionnaire.
مقاله انگلیسی
8 Mining productive-periodic frequent patterns in tele-health systems
کاوش الگوهای تکراری دوره ای تولیدی در سیستم های بهداشت و درمان-2018
Recently, tele-health systems have gained attention from vast research fields because they facilitate remote monitoring of patients (e.g. vital sign data, physical activities, etc.) by utlizing various technologies such as body sensor network, wireless communications, multimedia and human-computer interactions without interrupting the quality of lifestyle. As tele-health generates a huge amount of healthcare data consisting of much useful information, finding hidden information from the data is an important task. The purpose of this work is to facilitate a real-time warning alarm in the context of tele-health remote monitoring using data mining techni ques. This can be utilized for the e-wellbeing applications, for example, rehabilitation, early identification of therapeutic issues and emergency warning. In particular, we focus on mining Productive Periodic frequent patterns from incremental databases (such as vital sign data of patients) for various decision makings. Exploring the correlations between periodic frequent vital sign data or items is important since the inherent relationships between the items of patterns are relevant. To mine the correlated periodic frequent patterns from incremental databases, we introduce the productive (i.e. useful) periodic frequent patterns (PPFP) as the set of periodic frequent patterns with periodicities that result from the occurrence of correlated items. We finally design and develop an efficient PPFP mining technique that can mine the complete set of useful periodically occurring patterns in incremental databases. Numerous experiments were performed on both real and synthetic data set to judge the effectiveness of the proposed pattern mining procedure when contrasted with existing best in class approaches.
Keywords: Tele-health ، Data mining ، Productive periodic frequent patterns ، Periodic patterns ، Incremental database ، Fp-growth
مقاله انگلیسی
9 ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data
ViSiBiD: مدل های یادگیری برای کشف زودرس و پیش بینی زمان واقعی از حوادث بالینی شدید با استفاده از علائم حیاتی به عنوان داده های بزرگ-2017
The advance in wearable and wireless sensors technology have made it possible to monitor multiple vital signs (e.g. heart rate, blood pressure) of a patient anytime, anywhere. Vital signs are an essential part of daily monitoring and disease prevention. When multiple vital sign data from many patients are accumulated for a long period they evolve into big data. The objective of this study is to build a prognostic model, ViSiBiD, that can accurately identify dangerous clinical events of a home-monitoring patient in advance using knowledge learned from the patterns of multiple vital signs from a large number of similar patients. We developed an innovative technique that amalgamates existing data mining methods with smartly extracted features from vital sign correlations, and demonstrated its effectiveness on cloud platforms through comparative evaluations that showed its potential to become a new tool for predictive healthcare. Four clinical events are identified from 4893 patient records in publicly available databases where six bio-signals deviate from normality and different features are extracted prior to 1–2 h from 10 to 30 min observed data of those events. Known data mining algorithms along with some MapReduce implementations have been used for learning on a cloud platform. The best accuracy (95.85%) was obtained through a Random Forest classifier using all features. The encouraging learning performance using hybrid feature space proves the existence of discriminatory patterns in vital sign big data can identify severe clinical danger well ahead of time.
Keywords: Big data | Vital sign | Cloud computing | Correlations | Knowledge discovery | Data mining
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 8678 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 8678 :::::::: افراد آنلاین: 70