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https://www.readbyqxmd.com/read/29186052/cancer-classification-based-on-support-vector-machine-optimized-by-particle-swarm-optimization-and-artificial-bee-colony
#1
Lingyun Gao, Mingquan Ye, Changrong Wu
Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM...
November 29, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/29181235/systematic-review-of-data-mining-applications-in-patient-centered-mobile-based-information-systems
#2
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/29157454/developing-a-new-intelligent-system-for-the-diagnosis-of-tuberculous-pleural-effusion
#3
Chengye Li, Lingxian Hou, Bishundat Yanesh Sharma, Huaizhong Li, ChengShui Chen, Yuping Li, Xuehua Zhao, Hui Huang, Zhennao Cai, Huiling Chen
BACKGROUND AND OBJECTIVE: In countries with high prevalence of tuberculosis (TB), clinicians often diagnose tuberculous pleural effusion (TPE) by using diagnostic tests, which have not only poor sensitivity, but poor availability as well. The aim of our study is to develop a new artificial intelligence based diagnostic model that is accurate, fast, non-invasive and cost effective to diagnose TPE. It is expected that a tool derived based on the model be installed on simple computer devices (such as smart phones and tablets) and be used by clinicians widely...
January 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29134342/an-introduction-and-overview-of-machine-learning-in-neurosurgical-care
#4
REVIEW
Joeky T Senders, Mark M Zaki, Aditya V Karhade, Bliss Chang, William B Gormley, Marike L Broekman, Timothy R Smith, Omar Arnaout
BACKGROUND: Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models...
November 13, 2017: Acta Neurochirurgica
https://www.readbyqxmd.com/read/29129011/embodiment-and-estrangement-results-from-first-in-human-intelligent-bci-trial
#5
F Gilbert, M Cook, T O'Brien, J Illes
While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from the first-in-human clinical trial to study patients with implanted BCI advisory devices. We explored perceptions of self-change across six patients who volunteered to be implanted with artificially intelligent BCI devices. We used qualitative methodological tools grounded in phenomenology to conduct in-depth, semi-structured interviews...
November 11, 2017: Science and Engineering Ethics
https://www.readbyqxmd.com/read/29127485/oct-based-deep-learning-algorithm-for-the-evaluation-of-treatment-indication-with-anti-vascular-endothelial-growth-factor-medications
#6
Philipp Prahs, Viola Radeck, Christian Mayer, Yordan Cvetkov, Nadezhda Cvetkova, Horst Helbig, David Märker
PURPOSE: Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respective indications. Optical coherence tomography (OCT) scans of the central retina provide detailed anatomical data and are widely used by clinicians in the decision-making process of anti-VEGF indication. In recent years, significant progress has been made in artificial intelligence and computer vision research. We trained a deep convolutional artificial neural network to predict treatment indication based on central retinal OCT scans without human intervention...
November 10, 2017: Graefe's Archive for Clinical and Experimental Ophthalmology
https://www.readbyqxmd.com/read/29126825/artificial-intelligence-in-medical-practice-the-question-to-the-answer
#7
REVIEW
D Douglas Miller, Eric W Brown
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society - forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence...
November 7, 2017: American Journal of Medicine
https://www.readbyqxmd.com/read/29117178/predictable-response-finding-optimal-drugs-and-doses-using-artificial-intelligence
#8
Shraddha Chakradhar
No abstract text is available yet for this article.
November 7, 2017: Nature Medicine
https://www.readbyqxmd.com/read/29109070/artificial-intelligence-learning-semantics-via-external-resources-for-classifying-diagnosis-codes-in-discharge-notes
#9
Chin Lin, Chia-Jung Hsu, Yu-Sheng Lou, Shih-Jen Yeh, Chia-Cheng Lee, Sui-Lung Su, Hsiang-Cheng Chen
BACKGROUND: Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE: Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes...
November 6, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/29106268/strategy-selection-as-rational-metareasoning
#10
Falk Lieder, Thomas L Griffiths
Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question of how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy's performance...
November 2017: Psychological Review
https://www.readbyqxmd.com/read/29105694/-handle-with-care-about-the-potential-unintended-consequences-of-oracular-artificial-intelligence-systems-in-medicine
#11
Federico Cabitza, Camilla Alderighi, Raffaele Rasoini, Gian Franco Gensini
Decisional support systems based on machine learning (ML) in medicine are gaining a growing interest as some recent articles have highlighted the high diagnostic accuracy exhibited by these systems in specific medical contexts. However, it is implausible that any potential advantage can be obtained without some potential drawbacks. In light of the current gaps in medical research about the side effects of the application of these new AI systems in medical practice, in this article we summarize the main unexpected consequences that may result from the widespread application of "oracular" systems, that is highly accurate systems that cannot give reasonable explanations of their advice as those endowed with predictive models developed with ML techniques usually are...
October 2017: Recenti Progressi in Medicina
https://www.readbyqxmd.com/read/29101008/use-of-a-neural-net-to-model-the-impact-of-optical-coherence-tomography-abnormalities-on-vision-in-age-related-macular-degeneration
#12
Tariq M Aslam, Haider R Zaki, Sajjad Mahmood, Zaria C Ali, Nur A Ahmad, Mariana R Thorell, Konstantinos Balaskas
PURPOSE: To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age related macular degeneration and to demonstrate its use to model the impact of specific controlled OCT changes on vision. DESIGN: Artificial Intelligence (neural network) study. METHODS: We assessed 1400 OCT scans of patients with neovascular age related macular degeneration (AMD). 15 physical features for each eligible OCT as well as patient age were used as input data and corresponding recorded visual acuity as the target data to train, validate and test a supervised neural network...
October 31, 2017: American Journal of Ophthalmology
https://www.readbyqxmd.com/read/29097902/using-a-bayesian-network-to-predict-l5-s1-spinal-compression-force-from-posture-hand-load-anthropometry-and-disc-injury-status
#13
Richard E Hughes
Stochastic biomechanical modeling has become a useful tool most commonly implemented using Monte Carlo simulation, advanced mean value theorem, or Markov chain modeling. Bayesian networks are a novel method for probabilistic modeling in artificial intelligence, risk modeling, and machine learning. The purpose of this study was to evaluate the suitability of Bayesian networks for biomechanical modeling using a static biomechanical model of spinal forces during lifting. A 20-node Bayesian network model was used to implement a well-established static two-dimensional biomechanical model for predicting L5/S1 compression and shear forces...
2017: Applied Bionics and Biomechanics
https://www.readbyqxmd.com/read/29095571/generative-recurrent-networks-for-de-novo-drug-design
#14
Anvita Gupta, Alex T Müller, Berend J H Huisman, Jens A Fuchs, Petra Schneider, Gisbert Schneider
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy...
November 2, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/29080956/validation-of-an-online-risk-calculator-for-the-prediction-of-anastomotic-leak-after-colon-cancer-surgery-and-preliminary-exploration-of-artificial-intelligence-based-analytics
#15
T Sammour, L Cohen, A I Karunatillake, M Lewis, M J Lawrence, A Hunter, J W Moore, M L Thomas
BACKGROUND: Recently published data support the use of a web-based risk calculator ( www.anastomoticleak.com ) for the prediction of anastomotic leak after colectomy. The aim of this study was to externally validate this calculator on a larger dataset. METHODS: Consecutive adult patients undergoing elective or emergency colectomy for colon cancer at a single institution over a 9-year period were identified using the Binational Colorectal Cancer Audit database. Patients with a rectosigmoid cancer, an R2 resection, or a diverting ostomy were excluded...
October 28, 2017: Techniques in Coloproctology
https://www.readbyqxmd.com/read/29080669/personality-biomarkers-of-pathological-gambling-a-machine-learning-study
#16
Antonio Cerasa, Danilo Lofaro, Paolo Cavedini, Iolanda Martino, Antonella Bruni, Alessia Sarica, Domenico Mauro, Giuseppe Merante, Ilaria Rossomanno, Maria Rizzuto, Antonio Palmacci, Benedetta Aquino, Pasquale De Fazio, Giampaolo R Perna, Elena Vanni, Giuseppe Olivadese, Domenico Conforti, Gennarina Arabia, Aldo Quattrone
BACKGROUND: The application of artificial intelligence to extract predictors of Gambling disorder (GD) is a new field of study. A plethora of studies have suggested that maladaptive personality dispositions may serve as risk factors for GD. NEW METHOD: Here, we used Classification and Regression Trees algorithm to identify multivariate predictive patterns of personality profiles that could identify GD patients from healthy controls at an individual level. Forty psychiatric patients, recruited from specialized gambling clinics, without any additional comorbidity and 160 matched healthy controls completed the Five-Factor model of personality as measured by the NEO-PI-R, which were used to build the classification model...
November 1, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/29066576/real-time-differentiation-of-adenomatous-and-hyperplastic-diminutive-colorectal-polyps-during-analysis-of-unaltered-videos-of-standard-colonoscopy-using-a-deep-learning-model
#17
Michael F Byrne, Nicolas Chapados, Florian Soudan, Clemens Oertel, Milagros Linares Pérez, Raymond Kelly, Nadeem Iqbal, Florent Chandelier, Douglas K Rex
BACKGROUND: In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of 'resect and discard'. STUDY DESIGN AND METHODS: We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps...
October 24, 2017: Gut
https://www.readbyqxmd.com/read/29052630/mastering-the-game-of-go-without-human-knowledge
#18
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules...
October 18, 2017: Nature
https://www.readbyqxmd.com/read/29047393/the-human-behaviour-change-project-harnessing-the-power-of-artificial-intelligence-and-machine-learning-for-evidence-synthesis-and-interpretation
#19
Susan Michie, James Thomas, Marie Johnston, Pol Mac Aonghusa, John Shawe-Taylor, Michael P Kelly, Léa A Deleris, Ailbhe N Finnerty, Marta M Marques, Emma Norris, Alison O'Mara-Eves, Robert West
BACKGROUND: Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support...
October 18, 2017: Implementation Science: IS
https://www.readbyqxmd.com/read/29037014/deep-into-the-brain-artificial-intelligence-in-stroke-imaging
#20
REVIEW
Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, Dong-Wha Kang
Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner...
September 2017: Journal of Stroke
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