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https://www.readbyqxmd.com/read/28055930/deep-learning-for-health-informatics
#1
Daniele Ravi, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu Perez, Benny Lo, Guang-Zhong Yang
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data...
December 29, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27974883/artificial-neural-network-and-genetic-algorithm-hybrid-intelligence-for-predicting-thai-stock-price-index-trend
#2
Montri Inthachot, Veera Boonjing, Sarun Intakosum
This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction...
2016: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/27920762/artificial-intelligence-vs-statistical-modeling-and-optimization-of-continuous-bead-milling-process-for-bacterial-cell-lysis
#3
Shafiul Haque, Saif Khan, Mohd Wahid, Sajad A Dar, Nipunjot Soni, Raju K Mandal, Vineeta Singh, Dileep Tiwari, Mohtashim Lohani, Mohammed Y Areeshi, Thavendran Govender, Hendrik G Kruger, Arshad Jawed
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery...
2016: Frontiers in Microbiology
https://www.readbyqxmd.com/read/27919375/software-intelligent-system-for-effective-solutions-for-hearing-impaired-subjects
#4
Rajkumar S, Muttan S, Sapthagirivasan V, Jaya V, Vignesh S S
PURPOSE: The anatomy and physiology of the ear is complex in nature, which makes it a challenge for audiologists to prescribe solutions for varied hearing-impaired subjects. There is a need to increase the satisfaction level of hearing-aid users by adopting better strategies that involve modern technological advancements. AIM: To design and develop a decision support Software Intelligent System (SIS) that performs audiological investigations to assess the degree of hearing loss and to suggest appropriate hearing-aid gain values...
January 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/27845487/health-data-entanglement-and-artificial-intelligence-based-analysis-a-brand-new-methodology-to-improve-the-effectiveness-of-healthcare-services
#5
A Capone, A Cicchetti, F S Mennini, A Marcellusi, G Baio, G Favato
Healthcare expenses will be the most relevant policy issue for most governments in the EU and in the USA. This expenditure can be associated with two major key categories: demographic and economic drivers. Factors driving healthcare expenditure were rarely recognised, measured and comprehended. An improvement of health data generation and analysis is mandatory, and in order to tackle healthcare spending growth, it may be useful to design and implement an effective, advanced system to generate and analyse these data...
September 2016: La Clinica Terapeutica
https://www.readbyqxmd.com/read/27842598/exploratory-analysis-of-real-personal-emergency-response-call-conversations-considerations-for-personal-emergency-response-spoken-dialogue-systems
#6
Victoria Young, Elizabeth Rochon, Alex Mihailidis
BACKGROUND: The purpose of this study was to derive data from real, recorded, personal emergency response call conversations to help improve the artificial intelligence and decision making capability of a spoken dialogue system in a smart personal emergency response system. The main study objectives were to: develop a model of personal emergency response; determine categories for the model's features; identify and calculate measures from call conversations (verbal ability, conversational structure, timing); and examine conversational patterns and relationships between measures and model features applicable for improving the system's ability to automatically identify call model categories and predict a target response...
November 14, 2016: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/27830257/public-health-and-epidemiology-informatics
#7
A Flahault, A Bar-Hen, N Paragios
OBJECTIVES: The aim of this manuscript is to provide a brief overview of the scientific challenges that should be addressed in order to unlock the full potential of using data from a general point of view, as well as to present some ideas that could help answer specific needs for data understanding in the field of health sciences and epidemiology. METHODS: A survey of uses and challenges of big data analyses for medicine and public health was conducted. The first part of the paper focuses on big data techniques, algorithms, and statistical approaches to identify patterns in data...
November 10, 2016: Yearbook of Medical Informatics
https://www.readbyqxmd.com/read/27816264/artificial-intelligence-tools-for-scaling-up-of-high-shear-wet-granulation-process
#8
Mariana Landin
The results presented in this article demonstrate the potential of artificial intelligence tools for predicting the endpoint of the granulation process in high-speed mixer granulators of different scales from 25L to 600L. The combination of neurofuzzy logic and gene expression programing technologies allowed the modeling of the impeller power as a function of operation conditions and wet granule properties, establishing the critical variables that affect the response and obtaining a unique experimental polynomial equation (transparent model) of high predictability (R(2) > 86...
January 2017: Journal of Pharmaceutical Sciences
https://www.readbyqxmd.com/read/27810249/microwave-assisted-chemical-pre-treatment-of-waste-sorghum-leaves-process-optimization-and-development-of-an-intelligent-model-for-determination-of-volatile-compound-fractions
#9
Daneal C S Rorke, Terence N Suinyuy, E B Gueguim Kana
This study reports the profiling of volatile compounds generated during microwave-assisted chemical pre-treatment of sorghum leaves. Compounds including acetic acid (0-186.26ng/g SL), furfural (0-240.80ng/g SL), 5-hydroxymethylfurfural (HMF) (0-19.20ng/g SL) and phenol (0-7.76ng/g SL) were detected. The reducing sugar production was optimized. An intelligent model based on Artificial Neural Networks (ANNs) was developed and validated to predict a profile of 21 volatile compounds under novel pre-treatment conditions...
January 2017: Bioresource Technology
https://www.readbyqxmd.com/read/27777555/the-predictive-processing-paradigm-has-roots-in-kant
#10
Link R Swanson
Predictive processing (PP) is a paradigm in computational and cognitive neuroscience that has recently attracted significant attention across domains, including psychology, robotics, artificial intelligence and philosophy. It is often regarded as a fresh and possibly revolutionary paradigm shift, yet a handful of authors have remarked that aspects of PP seem reminiscent of the work of 18th century philosopher Immanuel Kant. To date there have not been any substantive discussions of how exactly PP links back to Kant...
2016: Frontiers in Systems Neuroscience
https://www.readbyqxmd.com/read/27766520/application-of-artificial-neural-network-model-combined-with-four-biomarkers-in-auxiliary-diagnosis-of-lung-cancer
#11
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
https://www.readbyqxmd.com/read/27752272/predicting-metabolic-syndrome-using-decision-tree-and-support-vector-machine-methods
#12
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
https://www.readbyqxmd.com/read/27742636/drug-concentration-thresholds-predictive-of-therapy-failure-and-death-in-children-with-tuberculosis-bread-crumb-trails-in-random-forests
#13
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
https://www.readbyqxmd.com/read/27693769/outcomes-and-complications-following-endovascular-treatment-of-brain-arteriovenous-malformations-a-prognostication-attempt-using-artificial-intelligence
#14
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
https://www.readbyqxmd.com/read/27620691/reproductive-endocrinology-and-musth-indicators-in-a-captive-asian-elephant-elephas-maximus
#15
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...
December 2016: Psychological Reports
https://www.readbyqxmd.com/read/27604658/perfect-24-h-management-of-hypertension-clinical-relevance-and-perspectives
#16
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
https://www.readbyqxmd.com/read/27598687/observational-learning-computations-in-neurons-of-the-human-anterior-cingulate-cortex
#17
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...
September 6, 2016: Nature Communications
https://www.readbyqxmd.com/read/27563724/strength-is-in-numbers-can-concordant-artificial-listeners-improve-prediction-of-emotion-from-speech
#18
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
https://www.readbyqxmd.com/read/27528266/the-non-linear-child-ontogeny-isoniazid-concentration-and-nat2-genotype-modulate-enzyme-reaction-kinetics-and-metabolism
#19
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
https://www.readbyqxmd.com/read/27458224/artificial-intelligence-and-amikacin-exposures-predictive-of-outcomes-in-multidrug-resistant-tuberculosis-patients
#20
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
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