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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 |