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Innate underpinnings of local adiposity distribution in Africa People in america: Exams in the Garcia Center Research.
Atrial Fibrillation (AF) is a cardiac condition resulting from uncoordinated contraction of the atria which may lead to an increase in the risk of heart attacks, strokes, and death. AF symptoms may go undetected and may require longterm monitoring of electrocardiogram (ECG) to be detected. Long-term ECG monitoring can generate a large amount of data which can increase power, storage, and the wireless transmission bandwidth of monitoring devices. Compressive Sensing (CS) is compression technique at the sampling stage which may save power, storage, and wireless bandwidth of monitoring devices. The reconstruction of compressive sensed ECG is a computationally expensive operation; therefore, detection of AF in compressive sensed ECG is warranted. This paper presents preliminary results of using deep learning to detect AF in deterministic compressive sensed ECG. MobileNetV2 convolutional neural network (CNN) was used in this paper. Transfer learning was utilized to leverage a pre-trained CNN with the final two layers retrained using 24 records from the Long-Term Atrial Fibrillation Database. The Short-Term Fourier Transform was used to generate spectrograms that were fed to the CNN. The CNN was tested on the MIT-BIH Atrial Fibrillation Database at the uncompressed, 50%, 75%, and 95% compressed ECG. The performance of the CNN was evaluated using weighted average precision (AP) and area under the curve (AUC) of the receiver operator curve (ROC). The CNN had AP of 0.80, 0.70, 0.70, and 0.57 at uncompressed, 50%, 75%, and 95% compression levels. The AUC was 0.87, 0.78, 0.79, and 0.75 at each compression level. The preliminary results show promise for using deep learning to detect AF in compressive sensed ECG.Clinical Relevance-This paper confirms that AF can be detected in compressive sensed ECG using deep learning, This will facilitate long-term ECG monitoring using wearable devices and will reduce adverse complications resulting from undiagnosed AF.The breast cancer is a prevalent problem that undermines quality of patients' lives and causes significant impacts on psychosocial wellness. Advanced sensing provides unprecedented opportunities to develop smart cancer care. The available sensing data captured from individuals enable the extraction of information pertinent to the breast cancer conditions to construct efficient and personalized intervention and treatment strategies. This research develops a novel sequential decision-making framework to determine optimal intervention and treatment planning for breast cancer patients. We design a Markov decision process (MDP) model for both objectives of intervention and treatment costs as well as quality adjusted life years (QALYs) with the data-driven and state-dependent intervention and treatment actions. The state space is defined as a vector of age, health status, prior intervention, and treatment plans. Also, the action space includes wait, prophylactic surgery, radiation therapy, chemotherapy, and their combinations. Experimental results demonstrate that prophylactic mastectomy and chemotherapy are more effective than other intervention and treatment plans in minimizing the expected cancer cost of 25 to 60 years-old patient with in-situ stage of cancer. However, wait policy leads to an optimal quality of life for a patient with the same state. The proposed MDP framework can also be generally applicable to a variety of medical domains that entail evidence-based decision making.Pectus Excavatum (PE) is a congenital anomaly of the ribcage, at the level of the sterno-costal plane, which consists of an inward angle of the sternum, in the direction of the spine. PE is the most common of all thoracic malformations, with an incidence of 1 in 300-400 people. To monitor the progress of the pathology, severity indices, or thoracic indices, have been used over the years. Among these indices, recent studies focus on the calculation of optical measures, calculated on the optical scan of the patient's chest, which can be very accurate without exposing the patient to invasive treatments such as CT scans. In this work, data from a sample of PE patients and corresponding doctors' severity assessments have been collected and used to create a decision tool to automatically assign a severity value to the patient. The idea is to provide the physician with an objective and easy to use measuring instrument that can be exploited in an outpatient clinic context. Among several classification tools, a Probabilistic Neural Network was chosen for this task for its simple structure and learning mode.Fibrosis is a significant indication of chronic liver diseases often due to hepatitis C Virus. It is becoming a global concern as a result of the rapid increase in the number of HCV infected patients, the high cost and flaws associated with the assessment process of liver fibrosis. This study aims to determine the features that significantly contribute to the identification of the stages of liver fibrosis and to generate rules to assist physicians during the treatment of the patients as a clinically non-invasive approach. Also, the performance of different Multi-layered Perceptron (MLP), Random Forest, and Logistic Regression classifiers are estimated and compared for the full and reduced feature sets. Decision Tree produced 28 rules in contrast with previous research work where 98002 rules had been generated from the same dataset with an accuracy rate of approximately 99.97%. The resulting rules of this study achieved a prediction accuracy for the histological staging of liver fibrosis of 97.45%. Among all the machine learning methods, MLP achieved the highest accuracy rate.This paper investigates the association between consecutive ambient air pollution and Chronic Obstructive Pulmonary Disease (COPD) hospitalization in Chengdu China. The three-year (2015-2017) time series data for both ambient air pollutant concentrations and COPD hospitalizations in Chengdu are approved for the study. The big data statistic analysis shows that Air Quality Index (AQI) exceeded the lighted air polluted level in Chengdu region are mainly attributed to particulate matters (i.e., PM2.5 and PM10). The time series study for consecutive ambient air pollutant concentrations reveal that AQI, PM2.5, and PM10 are significantly positive correlated, especially when the number of consecutive polluted days is greater than nine days. The daily COPD hospitalizations for every 10 μg/m3 increase in PM2.5 and PM10 indicate that consecutive ambient air pollution can lead to an appearance of an elevation of COPD admissions, and also present that dynamic responses before and after the peak admission are different. Support Vector Regression (SVR) is then used to describe the dynamics of COPD hospitalizations to consecutive ambient air pollution. selleckchem These findings will be further developed for region specific, hospital early notifications of COPD in responses to consecutive ambient air pollution.Unfractionated heparin (UFH) is commonly used in the intensive care unit (ICU) to prevent blood clotting. Recently, many researchers focus on the development of data- driven methods to solve UFH related problems, which usually involves time series analysis. The performance of data-driven methods depends on whether the inter-correlation of attributes (or variables) in the dataset is closely examined and addressed. This study performs attribute selection, optimal time delay and inter-attributes relations on ICU time series data, in order to provide insights of time series data for UFH related problems. Medical records of 3211 patients with 22 attributes extracted from MIMIC (Medical Information Mart for Intensive Care) III database are used for the experiment. Experimental result shows that some of commonly selected attributes in the literature are less sensitive to the variations of UFH injection. link2 Furthermore, some attributes are inter-dependent, which can increase the complexity of data-driven models, implying that the number of attributes could be reduced. link3 There are 9 attributes found highly related and fast responding in 22 commonly used attributes. This study shows strong potential to provide clinicians with information about sensitive attributes that can help determine the UFH injection policy in ICU.We developed a method of estimating impactors of cognitive function (ICF) - such as anxiety, sleep quality, and mood - using computational voice analysis. Clinically validated questionnaires (VQs) were used to score anxiety, sleep and mood while salient voice features were extracted to train regression models with deep neural networks. Experiments with 203 subjects showed promising results with significant concordance correlation coefficients (CCC) between actual VQ scores and the predicted scores (0.46 = anxiety, 0.50 = sleep quality, 0.45 = mood).A large amount of data including joint kinematics, joint kinetics, clinical and functional measurements constitutes the clinical gait analysis basis which is a process whereby quantitative gait information are collected to aid in clinical decision-making. Therefore, better understanding the relationship between the biomechanical and clinical data for the knee osteoarthritis (OA) patient is for a relevant importance. It's the purpose of this paper, which aims to analyze and visualize the correlation structure between biomechanical characteristics and clinical symptoms, and thus to provide an additional knowledge from the coupling of these parameters that will be useful for the pathology assessment of knee-joint disease in the end-staged knee OA patients. We perform two multivariate statistical approaches, first, a Canonical Correlation Analysis (CCA) to assess the multivariate association and, second, a graphical- based representation of the multivariate correlation to better understand the association between these multivariate data. Results show the usefulness of using such multivariate approaches to highlight association and specific correlation structure between the features and to extract meaningful information.This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.
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