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Artificial intelligence prediction

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https://www.readbyqxmd.com/read/29448930/a-prediction-study-of-warfarin-individual-stable-dose-after-mechanical-heart-valve-replacement-adaptive-neural-fuzzy-inference-system-prediction
#1
Huan Tao, Qian Li, Qin Zhou, Jie Chen, Bo Fu, Jing Wang, Wenzhe Qin, Jianglong Hou, Jin Chen, Li Dong
BACKGROUND: It's difficult but urgent to achieve the individualized rational medication of the warfarin, we aim to predict the individualized warfarin stable dose though the artificial intelligent Adaptive neural-fuzzy inference system (ANFIS). METHODS: Our retrospective analysis based on a clinical database, involving 21,863 patients from 15 Chinese provinces who receive oral warfarin after the heart valve replacement. They were allocated into four groups: the external validation group (A group), the internal validation group (B group), training group (C group) and stratified training group (D group)...
February 15, 2018: BMC Surgery
https://www.readbyqxmd.com/read/29447493/vitamin-d-and-ferritin-correlation-with-chronic-neck-pain-using-standard-statistics-and-a-novel-artificial-neural-network-prediction-model
#2
Haytham Eloqayli, Ali Al-Yousef, Raid Jaradat
AIM: Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). METHODS: Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled...
February 15, 2018: British Journal of Neurosurgery
https://www.readbyqxmd.com/read/29424635/application-of-medium-optimization-tools-for-improving-recombinant-human-interferon-gamma-production-from-kluyveromyces-lactis
#3
Rajat Pandey, Nitin Kumar, Ashish A Prabhu, Venkata Dasu Veeranki
The present study is focused upon improving biomass of Kluyveromyces lactis (K. lactis) cells expressing recombinant human interferon gamma (hIFN-γ), with the aim of augmenting hIFN-γ concentration by using statistical and artificial intelligence approach. Optimization of medium components viz., lactose, yeast extract and trace elements was carried out with Box-behnken design (BBD) & artificial neural network linked genetic algorithm (ANN-GA) for maximizing biomass of recombinant K. lactis (objective function)...
February 9, 2018: Preparative Biochemistry & Biotechnology
https://www.readbyqxmd.com/read/29402533/artificial-intelligence-and-machine-learning-in-radiology-opportunities-challenges-pitfalls-and-criteria-for-success
#4
James H Thrall, Xiang Li, Quanzheng Li, Cinthia Cruz, Synho DO, Keith Dreyer, James Brink
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations...
January 31, 2018: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29402532/protecting-your-patients-interests-in-the-era-of-big-data-artificial-intelligence-and-predictive-analytics
#5
Patricia Balthazar, Peter Harri, Adam Prater, Nabile M Safdar
The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The increasing use of big data and artificial intelligence techniques demands a re-examination of these principles in light of the potential issues surrounding privacy, confidentiality, data ownership, informed consent, epistemology, and inequities. Patients have strong opinions about these issues. Radiologists have a fiduciary responsibility to protect the interest of their patients...
February 2, 2018: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29398494/machine-learning-in-medical-imaging
#6
Maryellen L Giger
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data...
February 2, 2018: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29378578/artificial-intelligence-on-the-identification-of-risk-groups-for-osteoporosis-a-general-review
#7
REVIEW
Agnaldo S Cruz, Hertz C Lins, Ricardo V A Medeiros, José M F Filho, Sandro G da Silva
INTRODUCTION: The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. METHODS: A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms "Neural Network", "Osteoporosis Machine Learning" and "Osteoporosis Neural Network"...
January 29, 2018: Biomedical Engineering Online
https://www.readbyqxmd.com/read/29375975/computational-intelligence-assisted-understanding-of-nature-inspired-superhydrophobic-behavior
#8
Xia Zhang, Bei Ding, Ran Cheng, Sebastian C Dixon, Yao Lu
In recent years, state-of-the-art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature-inspired superhydrophobic behavior...
January 2018: Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
https://www.readbyqxmd.com/read/29360648/artificial-intelligence-estimates-the-impact-of-human-papillomavirus-types-in-influencing-the-risk-of-cervical-dysplasia-recurrence-progress-toward-a-more-personalized-approach
#9
Giorgio Bogani, Antonino Ditto, Fabio Martinelli, Mauro Signorelli, Valentina Chiappa, Umberto Leone Roberti Maggiore, Francesca Taverna, Claudia Lombardo, Chiara Borghi, Cono Scaffa, Domenica Lorusso, Francesco Raspagliesi
The objective of this study was to determine whether the pretreatment human papillomavirus (HPV) genotype might predict the risk of cervical dysplasia persistence/recurrence. Retrospective analysis of prospectively collected data of consecutive 5104 women who underwent the HPV-DNA test were matched with retrospective data of women undergoing either follow-up or medical/surgical treatment(s) for genital HPV-related infection(s). Artificial neuronal network (ANN) analysis was used in order to weight the importance of different HPV genotypes in predicting cervical dysplasia persistence/recurrence...
January 22, 2018: European Journal of Cancer Prevention
https://www.readbyqxmd.com/read/29352978/use-of-multimodality-imaging-and-artificial-intelligence-for-diagnosis-and-prognosis-of-early-stages-of-alzheimer-s-disease
#10
REVIEW
Xiaonan Liu, Kewei Chen, Teresa Wu, David Weidman, Fleming Lure, Jing Li
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information...
January 10, 2018: Translational Research: the Journal of Laboratory and Clinical Medicine
https://www.readbyqxmd.com/read/29352006/machine-learning-in-cardiovascular-medicine-are-we-there-yet
#11
REVIEW
Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform...
January 19, 2018: Heart: Official Journal of the British Cardiac Society
https://www.readbyqxmd.com/read/29346328/comparison-of-svm-rf-and-elm-on-an-electronic-nose-for-the-intelligent-evaluation-of-paraffin-samples
#12
Hong Men, Songlin Fu, Jialin Yang, Meiqi Cheng, Yan Shi, Jingjing Liu
Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin...
January 18, 2018: Sensors
https://www.readbyqxmd.com/read/29342489/correction-artificial-intelligence-may-help-in-predicting-the-need-for-additional-surgery-after-endoscopic-resection-of-t1-colorectal-cancer
#13
Katsuro Ichimasa, Shin-Ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyachi
No abstract text is available yet for this article.
January 17, 2018: Endoscopy
https://www.readbyqxmd.com/read/29341027/developing-deep-learning-applications-for-life-science-and-pharma-industry
#14
Daniel Siegismund, Vasily Tolkachev, Stephan Heyse, Beate Sick, Oliver Duerr, Stephan Steigele
Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be 'game changing' for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning...
January 16, 2018: Drug Research
https://www.readbyqxmd.com/read/29339510/artificial-intelligence-exploration-of-unstable-protocells-leads-to-predictable-properties-and-discovery-of-collective-behavior
#15
Laurie J Points, James Ward Taylor, Jonathan Grizou, Kevin Donkers, Leroy Cronin
Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors...
January 16, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29335825/application-of-artificial-intelligence-using-a-convolutional-neural-network-for-detecting-gastric-cancer-in-endoscopic-images
#16
Toshiaki Hirasawa, Kazuharu Aoyama, Tetsuya Tanimoto, Soichiro Ishihara, Satoki Shichijo, Tsuyoshi Ozawa, Tatsuya Ohnishi, Mitsuhiro Fujishiro, Keigo Matsuo, Junko Fujisaki, Tomohiro Tada
BACKGROUND: Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. METHODS: A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer...
January 15, 2018: Gastric Cancer
https://www.readbyqxmd.com/read/29309734/methodologic-guide-for-evaluating-clinical-performance-and-effect-of-artificial-intelligence-technology-for-medical-diagnosis-and-prediction
#17
Seong Ho Park, Kyunghwa Han
The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics...
January 8, 2018: Radiology
https://www.readbyqxmd.com/read/29297898/-artificial-intelligence-in-medicine-limits-and-obstacles
#18
Eugenio Santoro
Data scientists and physicians are starting to use artificial intelligence (AI) even in the medical field in order to better understand the relationships among the huge amount of data coming from the great number of sources today available. Through the data interpretation methods made available by the recent AI tools, researchers and AI companies have focused on the development of models allowing to predict the risk of suffering from a specific disease, to make a diagnosis, and to recommend a treatment that is based on the best and most updated scientific evidence...
December 2017: Recenti Progressi in Medicina
https://www.readbyqxmd.com/read/29288867/differential-effects-of-childhood-neglect-and-abuse-during-sensitive-exposure-periods-on-male-and-female-hippocampus
#19
Martin H Teicher, Carl M Anderson, Kyoko Ohashi, Alaptagin Khan, Cynthia E McGreenery, Elizabeth A Bolger, Michael L Rohan, Gordana D Vitaliano
The hippocampus is a highly stress susceptible structure and hippocampal abnormalities have been reported in a host of psychiatric disorders including major depression and post-traumatic stress disorder (PTSD). The hippocampus appears to be particularly susceptible to early life stress with a graded reduction in volume based on number of types (multiplicity) or severity of maltreatment. We assessed whether the most important predictors of adult hippocampal volume were multiplicity, severity or duration of exposure or timing of maltreatment during developmental sensitive periods...
December 27, 2017: NeuroImage
https://www.readbyqxmd.com/read/29286945/an-interpretable-machine-learning-model-for-accurate-prediction-of-sepsis-in-the-icu
#20
Shamim Nemati, Andre Holder, Fereshteh Razmi, Matthew D Stanley, Gari D Clifford, Timothy G Buchman
OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. DESIGN: Observational cohort study. SETTING: Academic medical center from January 2013 to December 2015...
December 26, 2017: Critical Care Medicine
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