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نتیجه جستجو - Medical decision making

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 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
2 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
3 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
4 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
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