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A different sleep apnea classification system with neural network based on the acceleration signals
یک سیستم طبقه بندی sleep apnea متفاوت با شبکه عصبی مبتنی بر سیگنال های شتاب-2020 Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction
in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women
and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly,
the disabled, and pediatric sleep apnea patients.
Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical
system (MEMS) based acceleration sensor. It records the value of acceleration by measuring
the movements of the diaphragm in three axes during the respiratory. The measurements are
carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement
results. An artificial neural network model was designed to determine the apnea event. For the number
of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden
neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that
three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer.
Results: A study group was formed of 5 patients (having different characteristics (age, height, and body
weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration
data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and
detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train
an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then
Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data.
Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration
values. Measurements are performed by the MEMS-based accelerometer and Industrial
Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients.
The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with
1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check
the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark
shows us that trained ANN successfully detects apnea events. One of the contributions of this study
to literature is that only ACC data are used in the ANN training step. After training for one patient, the
ANN system can monitor the apnea event situation on-line for others. Keywords: Sleep apnea | Acceleration sensor | Acceleration data | Artificial neural network | Medical decision making |
مقاله انگلیسی |
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Decision based on big data research for non-small cell lung cancer in medical artificial system in developing country
تصمیم گیری بر اساس تحقیقات داده های بزرگ برای سرطان ریه های غیر سلولی در سیستم های پزشکی مصنوعی در کشورهای در حال توسعه-2018 Non-small cell lung cancer (NSCLC) is a high risk cancer and is usually scanned by PET–CT for testing,
predicting and then give the treatment methods. However, in the actual hospital system, at least 640
images must be generated for each patient through PET–CT scanning. Especially in developing countries,
a huge number of patients in NSCLC are attended by doctors. Artificial system can predict and make
decision rapidly. According to explore and research artificial medical system, the selection of artificial ob
servations also can result in low work efficiency for doctors. In this study, data information of 2,789,675
patients in three hospitals in China are collected, compiled, and used as the research basis; these data
are obtained through image acquisition and diagnostic parameter machine decision-making method on
the basis of the machine diagnosis and medical system design model of adjuvant therapy. By combin
ing image and diagnostic parameters, the machine decision diagnosis auxiliary algorithm is established.
Experimental result shows that the accuracy has reached 77% in NSCLC.
Keywords: NSCLC ، PET–CT ، Medical decision making ، Big data ، Image ، Diagnostic parameters |
مقاله انگلیسی |
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Review of the Society of Thoracic Surgeons Congenital Heart Surgery Database: 2017 Update on Outcomes and Quality Implications for the Anesthesiologist
مرور پایگاه داده جراحی انجمن جراحان قلب مادرزادی : 2017 به روز رسانی نتایج و پیامدهای کیفیت برای متخصص بیهوشی-2017 The pathway to current state-of-the-art congenital cardiac perioperative care has been paved
by pioneering heart surgery, the development of dedicated cardiac intensive care units, as well
as congenital cardiology and anesthesiology care teams. We work in an era of large datasets
where data collection and analysis influence medical decision making. After three decades of
data collection, analysis and subsequent changes in clinical management, progress is being
made in developing data-driven care protocols in patients with congenital heart disease.1 The
term standardization in reference to medical care can be misinterpreted as a “one size fits all” mentality. Medical care should be individualized to a specific patient, whereas data analyses
and review of patient outcomes potentially decrease unnecessary variation in care.
Additionally, comparative data has helped influence important concepts such as center
transparency and public reporting in congenital heart surgery. Centers can access data from
the Society of Thoracic Surgeons website since January 2015.
Outcomes | quality | database | congenital heart disease | anesthesia | cardiac surgery |
مقاله انگلیسی |
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Predicting overall survivability in comorbidity of cancers: A data mining approach
پیش بینی بقای کلی در اختلال همبودی سرطان: یک رویکرد داده کاوی-2015 Cancer and other chronic diseases have constituted (and will do so at an increasing pace) a significant portion of healthcare costs in the United States in recent years. Although prior research has shown that diagnostic and treat- ment recommendations might be altered based on the severity of comorbidities, chronic diseases are still being investigated in isolation from one another in most cases. To illustrate the significance of concurrent chronic dis- eases in the course of treatment, this study uses SEER's cancer data to create two comorbid data sets: one for breast and female genital cancers and another for prostate and urinal cancers. Several popular machine learning techniques are then applied to the resultant data sets to build predictive models. Comparison of the results shows that having more information about comorbid conditions of patients can improve models' predictive power, which in turn, can help practitioners make better diagnostic and treatment decisions. Therefore, proper identifi- cation, recording, and use of patients' comorbidity status can potentially lower treatment costs and ease the healthcare related economic challenges.© 2015 Elsevier B.V. All rights reserved.
Keywords: Medical decision making | Comorbidity | Concurrent diseases | Concomitant diseases | Predictive modeling | Random forest |
مقاله انگلیسی |