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Artificial intelligence prediction

Xiaoran Duan, Yongli Yang, Shanjuan Tan, Sihua Wang, Xiaolei Feng, Liuxin Cui, Feifei Feng, Songcheng Yu, Wei Wang, Yongjun Wu
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length...
October 20, 2016: Medical & Biological Engineering & Computing
Farzaneh Karimi-Alavijeh, Saeed Jalili, Masoumeh Sadeghi
BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome...
May 2016: ARYA Atherosclerosis
Soumya Swaminathan, Jotam G Pasipanodya, Geetha Ramachandran, A K Hemanth Kumar, Shashikant Srivastava, Devyani Deshpande, Eric Nuermberger, Tawanda Gumbo
BACKGROUND:  The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown. METHODS:  Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables...
November 1, 2016: Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America
Hamed Asadi, Hong Kuan Kok, Seamus Looby, Paul Brennan, Alan O'Hare, John Thornton
PURPOSE: This study aims to identify factors influencing outcome in brain arteriovenous malformations (BAVM) treated with endovascular embolisation. We also assessed the feasibility of using machine learning techniques to prognosticate and predict outcome and compared this to conventional statistical analyses. METHODS: A retrospective study of patients undergoing endovascular treatment of BAVM over a 22-year period in a national neuroscience centre was performed...
September 28, 2016: World Neurosurgery
Connie Duer, Tom Tomasi, Charles I Abramson
Even in the best situations, the artificial social constructs of captivity alter natural elephant behavior and unfortunately create distress. Asian elephants are powerful and intelligent animals that require consideration for their well-being and prudent management. The males present particular difficulties due to a temporary state of heightened aggressive behavior unique to male elephants called "musth." When he is in this state, the danger the elephant poses to other animals and the people around him is considerable...
September 12, 2016: Psychological Reports
K Kario
Out-of-office blood pressure (BP) measured by home BP monitoring, or ambulatory BP monitoring, was demonstrated to be superior to office BP for the prediction of cardiovascular events. The J-HOP study of a nationwide Japanese cohort demonstrated that morning home BP is the best stroke predictor. In the prospective HONEST study of >21 000 hypertensives, on-treatment morning home BP was shown to be a strong predictor both of future coronary artery disease and stroke events. In subjects whose office BP was maintained at ⩾150 mm Hg, there was no increase in cardiovascular events when their morning systolic BP was well-controlled at <125 mm Hg...
September 8, 2016: Journal of Human Hypertension
Michael R Hill, Erie D Boorman, Itzhak Fried
When learning from direct experience, neurons in the primate brain have been shown to encode a teaching signal used by algorithms in artificial intelligence: the reward prediction error (PE)-the difference between how rewarding an event is, and how rewarding it was expected to be. However, in humans and other species learning often takes place by observing other individuals. Here, we show that, when humans observe other players in a card game, neurons in their rostral anterior cingulate cortex (rACC) encode both the expected value of an observed choice, and the PE after the outcome was revealed...
2016: Nature Communications
Eugenio Martinelli, Arianna Mencattini, Elena Daprati, Corrado Di Natale
Humans can communicate their emotions by modulating facial expressions or the tone of their voice. Albeit numerous applications exist that enable machines to read facial emotions and recognize the content of verbal messages, methods for speech emotion recognition are still in their infancy. Yet, fast and reliable applications for emotion recognition are the obvious advancement of present 'intelligent personal assistants', and may have countless applications in diagnostics, rehabilitation and research. Taking inspiration from the dynamics of human group decision-making, we devised a novel speech emotion recognition system that applies, for the first time, a semi-supervised prediction model based on consensus...
2016: PloS One
Zoe Rogers, Hiwot Hiruy, Jotam G Pasipanodya, Chris Mbowane, John Adamson, Lihle Ngotho, Farina Karim, Prakash Jeena, William Bishai, Tawanda Gumbo
N-acetyltransferase 2 (NAT2) catalyzes the acetylation of isoniazid to N-acetylisoniazid. NAT2 polymorphism explains 88% of isoniazid clearance variability in adults. We examined the effects of clinical and genetic factors on Michaelis-Menten reaction kinetic constants of maximum velocity (Vmax) and affinity (Km) in children 0-10years old. We measured the rates of isoniazid elimination and N-acetylisoniazid production in the blood of 30 children. Since maturation effects could be non-linear, we utilized a pharmacometric approach and the artificial intelligence method, multivariate adaptive regression splines (MARS), to identify factors predicting NAT2 Vmax and Km by examining clinical, genetic, and laboratory factors in toto...
September 2016: EBioMedicine
Chawangwa Modongo, Jotam G Pasipanodya, Beki T Magazi, Shashikant Srivastava, Nicola M Zetola, Scott M Williams, Giorgio Sirugo, Tawanda Gumbo
Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR-TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofloxacin, ethionamide, cycloserine, and pyrazinamide and calculated areas under the concentration-time curves from 0 to 24 h (AUC0-24). The patients were followed monthly for sputum culture conversion based on liquid cultures. The median duration of amikacin therapy was 184 (range, 28 to 866) days, at a median dose of 17...
October 2016: Antimicrobial Agents and Chemotherapy
Rania M Hathout, Abdelkader A Metwally
This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e...
July 20, 2016: European Journal of Pharmaceutics and Biopharmaceutics
Martin V Butz
This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference...
2016: Frontiers in Psychology
Khalid A M Attia, Mohammed W I Nassar, Mohamed B El-Zeiny, Ahmed Serag
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm...
January 5, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
Michael E Brier, Adam E Gaweda
Computational intelligence for the prediction of hemoglobin to guide the selection of erythropoiesis-stimulating agent dose results in improved anemia management. The models used for the prediction result from the use of individual patient data and help to increase the number of hemoglobin observations within the target range. The benefits of using these modeling techniques appear to be a decrease in erythropoiesis-stimulating agent use and a decrease in the number of transfusions. This study confirms the results of previous smaller studies and suggests that additional beneficial results may be achieved...
August 2016: Kidney International
Charalampos Siristatidis, Paraskevi Vogiatzi, Abraham Pouliakis, Marialenna Trivella, Nikolaos Papantoniou, Stefano Bettocchi
AIM: To propose a functional in vitro fertilization (IVF) prediction model to assist clinicians in tailoring personalized treatment of subfertile couples and improve assisted reproduction outcome. MATERIALS AND METHODS: Construction and evaluation of an enhanced web-based system with a novel Artificial Neural Network (ANN) architecture and conformed input and output parameters according to the clinical and bibliographical standards, driven by a complete data set and "trained" by a network expert in an IVF setting...
July 2016: In Vivo
Stefano Parodi, Chiara Manneschi, Damiano Verda, Enrico Ferrari, Marco Muselli
This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms (k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method)...
June 27, 2016: Health Informatics Journal
A S Vickram, A Rao Kamini, Raja Das, M Ramesh Pathy, R Parameswari, K Archana, T B Sridharan
UNLABELLED: Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence...
August 2016: Systems Biology in Reproductive Medicine
Hossam M Zawbaa, Jakub Szlȩk, Crina Grosan, Renata Jachowicz, Aleksander Mendyk
Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected...
2016: PloS One
Mauro Castelli, Luca Manzoni, Aleš Popovič
Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data...
2016: Computational Intelligence and Neuroscience
Leon DeJournett, Jeremy DeJournett
BACKGROUND: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers...
June 14, 2016: Journal of Diabetes Science and Technology
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