keyword
MENU ▼
Read by QxMD icon Read
search

Artificial intelligence prediction

keyword
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
#1
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
#2
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
#3
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
#4
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...
November 2, 2016: 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
#5
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...
October 22, 2016: Bioresource Technology
https://www.readbyqxmd.com/read/27777555/the-predictive-processing-paradigm-has-roots-in-kant
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
https://www.readbyqxmd.com/read/27604658/perfect-24-h-management-of-hypertension-clinical-relevance-and-perspectives
#12
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
#13
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
https://www.readbyqxmd.com/read/27563724/strength-is-in-numbers-can-concordant-artificial-listeners-improve-prediction-of-emotion-from-speech
#14
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
#15
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
#16
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
https://www.readbyqxmd.com/read/27449631/towards-better-modelling-of-drug-loading-in-solid-lipid-nanoparticles-molecular-dynamics-docking-experiments-and-gaussian-processes-machine-learning
#17
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
https://www.readbyqxmd.com/read/27445895/toward-a-unified-sub-symbolic-computational-theory-of-cognition
#18
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
https://www.readbyqxmd.com/read/27423110/firefly-algorithm-versus-genetic-algorithm-as-powerful-variable-selection-tools-and-their-effect-on-different-multivariate-calibration-models-in-spectroscopy-a-comparative-study
#19
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
https://www.readbyqxmd.com/read/27418093/artificial-intelligence-for-optimal-anemia-management-in-end-stage-renal-disease
#20
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
keyword
keyword
22554
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"