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https://www.readbyqxmd.com/read/29216422/distinguishing-petroleum-crude-oil-and-fuel-from-smoke-exposure-within-populations-based-on-the-relative-blood-levels-of-benzene-toluene-ethylbenzene-and-xylenes-btex-styrene-and-2-5-dimethylfuran-by-pattern-recognition-using-artificial-neural-networks
#1
David Michael Chambers, Christopher Mark Reese, Lydia Grace Thornburg, Eduardo Sanchez, Jessica Patricia Rafson, Benjamin C Blount, John Russell Erskine Ruhl, Victor Raul De Jesus
Studies of exposure to petroleum (crude oil/fuel) often involve monitoring benzene, toluene, ethylbenzene, xylenes (BTEX) and styrene (BTEXS) because of their toxicity and gas-phase prevalence, where exposure is typically by inhalation. However, BTEXS levels in the general U.S. population are primarily from exposure to tobacco smoke, where smokers have blood levels on average up to eight times higher than nonsmokers. This work describes a method using partition theory and artificial neural network (ANN) pattern recognition to classify exposure source based on relative BTEXS and 2,5-dimethylfuran blood levels...
December 7, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/29211925/artificial-neural-networks-and-liver-transplantation-are-we-ready-for-self-driving-cars
#2
EDITORIAL
Allison J Kwong, Sumeet K Asrani
No abstract text is available yet for this article.
December 6, 2017: Liver Transplantation
https://www.readbyqxmd.com/read/29206856/a-novel-post-processing-scheme-for-two-dimensional-electrical-impedance-tomography-based-on-artificial-neural-networks
#3
Sébastien Martin, Charles T M Choi
OBJECTIVE: Electrical Impedance Tomography (EIT) is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time...
2017: PloS One
https://www.readbyqxmd.com/read/29203032/detection-and-prediction-of-driver-drowsiness-using-artificial-neural-network-models
#4
Charlotte Jacobé de Naurois, Christophe Bourdin, Anca Stratulat, Emmanuelle Diaz, Jean-Louis Vercher
Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110min under conditions optimized to induce drowsiness...
December 1, 2017: Accident; Analysis and Prevention
https://www.readbyqxmd.com/read/29198027/estimation-of-sodium-adsorption-ratio-indicator-using-data-mining-methods-a-case-study-in-urmia-lake-basin-iran
#5
Mohammad Taghi Sattari, Arya Farkhondeh, John Patrick Abraham
Water quality is a major concern around the world, particularly in dry climates. Usually, assessment of surface water quality is costly and time-consuming. In this situation, a method which could estimate the water quality accurately with the minimum of hydro-chemical parameters would be appealing. In this study, three data mining methods, namely, M5 model tree, support vector machine (SVM), and Gaussian process (GP), were employed to estimate the sodium adsorption ratio (SAR) indicator in the Shahrchay River located in the west of the Urmia Lake basin, Iran...
December 2, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/29196685/medical-examination-powers-mir-194-5p-as-a-biomarker-for-postmenopausal-osteoporosis
#6
Haifeng Ding, Jia Meng, Wei Zhang, Zhangming Li, Wenjing Li, Mingming Zhang, Ying Fan, Qiujun Wang, Yina Zhang, Lihong Jiang, Wenliang Zhu
An important attribute of microRNAs is their potential use as disease biomarkers. However, such applications may be restricted because of unsatisfactory performance of the microRNA of interest. Owing to moderate correlation with spine T-score, miR-194-5p was identified as a potential biomarker for postmenopausal osteoporosis. Here, we determined whether medical examination could improve its characteristic as a biomarker for postmenopausal osteoporosis. We recruited 230 postmenopausal Chinese women to measure circulating levels of miR-194-5p, determine the spine bone status, and perform a 42-item medical examination...
December 1, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29196623/evolving-autonomous-learning-in-cognitive-networks
#7
Leigh Sheneman, Arend Hintze
There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates...
December 1, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29195703/supervised-learning-methods-for-pathological-arterial-pulse-wave-differentiation-a-svm-and-neural-networks-approach
#8
Joana S Paiva, João Cardoso, Tânia Pereira
OBJECTIVE: The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. MATERIALS AND METHODS: The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients...
January 2018: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29193306/artificial-neural-networks-for-stiffness-estimation-in-magnetic-resonance-elastography
#9
Matthew C Murphy, Armando Manduca, Joshua D Trzasko, Kevin J Glaser, John Huston, Richard L Ehman
PURPOSE: To investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data. METHODS: Artificial neural networks were fit using model-based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy...
November 28, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/29193124/prediction-of-benzo-a-pyrene-content-of-smoked-sausage-using-back-propagation-artificial-neural-network
#10
Yan Chen, Kezhou Cai, Zehui Tu, Wen Nie, Tuo Ji, Bing Hu, Conggui Chen, Shaotong Jiang
BACKGROUND: Benzo[a]pyrene (BaP), a potent mutagen and carcinogen, is reported to be present in processed meat products and, in particular, in smoked meat. However, few methods exist for predictive determining the BaP content of smoked meats such as sausage. In this study, we used an artificial neural network (ANN) model based on the back-propagation (BP) algorithm to predict the BaP content of smoked sausage. RESULTS: The results showed that the BP network based on the Levenberg-Marquardt algorithm was the best suited for creating a nonlinear map between the input and output parameters...
November 29, 2017: Journal of the Science of Food and Agriculture
https://www.readbyqxmd.com/read/29190799/detecting-event-related-changes-in-organizational-networks-using-optimized-neural-network-models
#11
Ze Li, Duoyong Sun, Renqi Zhu, Zihan Lin
Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification...
2017: PloS One
https://www.readbyqxmd.com/read/29189496/artificial-neural-network-a-method-for-prediction-of-surgery-related-pressure-injury-in-cardiovascular-surgical-patients
#12
Hong-Lin Chen, Shi-Jia Yu, Yan Xu, Si-Qi Yu, Jia-Qi Zhang, Jing-Yi Zhao, Peng Liu, Bin Zhu
PURPOSE: The aim of this study was to build an artificial neural network (ANN) model for predicting surgery-related pressure injury (SRPI) in cardiovascular surgical patients. DESIGN: Prospective cohort study. SUBJECTS AND SETTING: One hundred forty-nine patients who had cardiovascular surgery were included in the study. This study was conducted in a 1000-bed teaching hospital in Eastern China where approximately 250 to 350 cardiac surgeries are performed each year...
November 16, 2017: Journal of Wound, Ostomy, and Continence Nursing
https://www.readbyqxmd.com/read/29188231/forward-and-reverse-mapping-for-milling-process-using-artificial-neural-networks
#13
Rashmi L Malghan, Karthik Rao M C, Arun Kumar Shettigar, Shrikantha S Rao, R J D'Souza
The data set presented is related to the milling process of AA6061-4.5%Cu-5%SiCp composite. The data primarily concentrates on predicting values of some machining responses, such as cutting force, surface finish and power utilization utilizing using forward back propagation neural network based approach, i.e. ANN based on three process parameters, such as spindle speed, feed rate and depth of cut.The comparing reverse model is likewise created to prescribe the ideal settings of processing parameters for accomplishing the desired responses as indicated by the necessities of the end clients...
February 2018: Data in Brief
https://www.readbyqxmd.com/read/29188217/prediction-of-the-thermal-conductivity-of-refrigerants-by-computational-methods-and-artificial-neural-network
#14
Forouzan Ghaderi, Amir H Ghaderi, Noushin Ghaderi, Bijan Najafi
Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density...
2017: Frontiers in Chemistry
https://www.readbyqxmd.com/read/29181237/prediction-of-kidney-graft-rejection-using-artificial-neural-network
#15
Leili Tapak, Omid Hamidi, Payam Amini, Jalal Poorolajal
Objectives: Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR). Methods: The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011...
October 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/29181235/systematic-review-of-data-mining-applications-in-patient-centered-mobile-based-information-systems
#16
Mina Fallah, Sharareh R Niakan Kalhori
Objectives: Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016...
October 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/29181071/application-of-artificial-neural-networks-for-predicting-presence-of-non-sentinel-lymph-node-metastases-in-breast-cancer-patients-with-positive-sentinel-lymph-node-biopsies
#17
Tomasz Nowikiewicz, Paweł Wnuk, Bogdan Małkowski, Andrzej Kurylcio, Janusz Kowalewski, Wojciech Zegarski
Introduction: The aim of this study was to present a new predictive tool for non-sentinel lymph node (nSLN) metastases. Material and methods: One thousand five hundred eighty-three patients with early-stage breast cancer were subjected to sentinel lymph node biopsy (SLNB) between 2004 and 2012. Metastatic SLNs were found in 348 patients - the retrospective group. Selective axillary lymph node dissection (ALND) was performed in 94% of cases. Involvement of the nSLNs was identified in 32...
October 2017: Archives of Medical Science: AMS
https://www.readbyqxmd.com/read/29179711/a-systematic-review-of-data-mining-and-machine-learning-for-air-pollution-epidemiology
#18
Colin Bellinger, Mohomed Shazan Mohomed Jabbar, Osmar Zaïane, Alvaro Osornio-Vargas
BACKGROUND: Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. METHODS: We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology...
November 28, 2017: BMC Public Health
https://www.readbyqxmd.com/read/29179052/using-phone-sensors-and-an-artificial-neural-network-to-detect-gait-changes-during-drinking-episodes-in-the-natural-environment
#19
Brian Suffoletto, Pedram Gharani, Tammy Chung, Hassan Karimi
BACKGROUND: Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concentration (eBAC). METHODS: Ten young adult heavy drinkers were prompted to complete a 5-step gait task every hour from 8pm to 12am over four consecutive weekends...
November 22, 2017: Gait & Posture
https://www.readbyqxmd.com/read/29176802/assessment-of-automated-analysis-of-portable-oximetry-as-a-screening-test-for-moderate-to-severe-sleep-apnea-in-patients-with-chronic-obstructive-pulmonary-disease
#20
Ana M Andrés-Blanco, Daniel Álvarez, Andrea Crespo, C Ainhoa Arroyo, Ana Cerezo-Hernández, Gonzalo C Gutiérrez-Tobal, Roberto Hornero, Félix Del Campo
BACKGROUND: The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities. OBJECTIVE: To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home...
2017: PloS One
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