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Artificial Intelligence in Medicine

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https://www.readbyqxmd.com/read/28818520/owlready-ontology-oriented-programming-in-python-with-automatic-classification-and-high-level-constructs-for-biomedical-ontologies
#1
Jean-Baptiste Lamy
OBJECTIVE: Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies...
August 14, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28755798/detecting-masses-in-dense-breast-using-independent-component-analysis
#2
Luis Claudio de Oliveira Silva, Allan Kardec Barros, Marcus Vinicius Lopes
Breast cancer is the second type of cancer that most affects women in the world, losing only for non-melanoma skin cancer. Breast density can hinder the location of masses, especially in early stages. In this work, the use of independent component analysis for detecting lesions in dense breasts is proposed. Several works suggest the use of computer aided diagnosis (CAD), increasing sensitivity to over 90% in detecting cancer in nondense breasts, however there are few published studies about detecting in dense breasts...
July 26, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28750949/a-machine-learning-approach-for-real-time-modelling-of-tissue-deformation-in-image-guided-neurosurgery
#3
Michele Tonutti, Gauthier Gras, Guang-Zhong Yang
OBJECTIVES: Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms...
July 24, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28774465/a-new-preprocessing-parameter-estimation-based-on-geodesic-active-contour-model-for-automatic-vestibular-neuritis-diagnosis
#4
Amine Ben Slama, Aymen Mouelhi, Hanene Sahli, Sondes Manoubi, Chiraz Mbarek, Hedi Trabelsi, Farhat Fnaiech, Mounir Sayadi
The diagnostic of the vestibular neuritis (VN) presents many difficulties to traditional assessment methods This paper deals with a fully automatic VN diagnostic system based on nystagmus parameter estimation using a pupil detection algorithm. A geodesic active contour model is implemented to find an accurate segmentation region of the pupil. Hence, the novelty of the proposed algorithm is to speed up the standard segmentation by using a specific mask located on the region of interest. This allows a drastically computing time reduction and a great performance and accuracy of the obtained results...
July 23, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28733120/employing-decomposable-partially-observable-markov-decision-processes-to-control-gene-regulatory-networks
#5
Utku Erdogdu, Faruk Polat, Reda Alhajj
OBJECTIVE: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs). METHODS AND MATERIAL: Different approaches exist to model GRNs; they are mostly simulated as mathematical models that represent relationships between genes. Actually, it has been realized that biological functions at the cellular level are controlled by genes; thus, by controlling the behavior of genes, it is possible to regulate these biological functions...
July 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28733119/artificial-intelligence-in-medicine-aime-2015
#6
EDITORIAL
John H Holmes, Lucia Sacchi, Riccardo Bellazzi, Niels Peek
No abstract text is available yet for this article.
July 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28712673/extract-critical-factors-affecting-the-length-of-hospital-stay-of-pneumonia-patient-by-data-mining-case-study-an-iranian-hospital
#7
Naghmeh Khajehali, Somayeh Alizadeh
MOTIVATION: Pneumonia is a prevalent infection of lower respiratory tract caused by infected lungs. Length of stay (LOS) in hospital is one of the simplest and most important indicators in hospital activity that is used for different purposes. The aim of this study is to explore the important factors affecting the LOS of patients with pneumonia in hospitals. METHODS: The clinical data set for the study were collected from 387 patients in a specialized hospital in Iran between 2009 and 2015...
July 13, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28709745/personal-sleep-pattern-visualization-using-sequence-based-kernel-self-organizing-map-on-sound-data
#8
Hongle Wu, Takafumi Kato, Tomomi Yamada, Masayuki Numao, Ken-Ichi Fukui
We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns...
July 11, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28583437/prediction-of-synergistic-anti-cancer-drug-combinations-based-on-drug-target-network-and-drug-induced-gene-expression-profiles
#9
Xiangyi Li, Yingjie Xu, Hui Cui, Tao Huang, Disong Wang, Baofeng Lian, Wei Li, Guangrong Qin, Lanming Chen, Lu Xie
OBJECTIVE: Synergistic drug combinations are promising therapies for cancer treatment. However, effective prediction of synergistic drug combinations is quite challenging as mechanisms of drug synergism are still unclear. Various features such as drug response, and target networks may contribute to prediction of synergistic drug combinations. In this study, we aimed to construct a computational model to predict synergistic drug combinations. METHODS: We designed drug physicochemical features and network features, including drug chemical structure similarity, target distance in protein-protein network and targeted pathway similarity...
June 2, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28559133/medical-image-classification-based-on-multi-scale-non-negative-sparse-coding
#10
Ruijie Zhang, Jian Shen, Fushan Wei, Xiong Li, Arun Kumar Sangaiah
With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers...
May 27, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28701276/medical-image-classification-via-multiscale-representation-learning
#11
Qiling Tang, Yangyang Liu, Haihua Liu
Multiscale structure is an essential attribute of natural images. Similarly, there exist scaling phenomena in medical images, and therefore a wide range of observation scales would be useful for medical imaging measurements. The present work proposes a multiscale representation learning method via sparse autoencoder networks to capture the intrinsic scales in medical images for the classification task. We obtain the multiscale feature detectors by the sparse autoencoders with different receptive field sizes, and then generate the feature maps by the convolution operation...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28662816/premature-ventricular-contraction-detection-combining-deep-neural-networks-and-rules-inference
#12
Fei-Yan Zhou, Lin-Peng Jin, Jun Dong
Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD)...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28655440/iacp-gaensc-evolutionary-genetic-algorithm-based-ensemble-classification-of-anticancer-peptides-by-utilizing-hybrid-feature-space
#13
Shahid Akbar, Maqsood Hayat, Muhammad Iqbal, Mian Ahmad Jan
Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28641924/random-survival-forest-with-space-extensions-for-censored-data
#14
Hong Wang, Lifeng Zhou
Prediction capability of a classifier usually improves when it is built from an extended variable space by adding new variables from randomly combination of two or more original variables. However, its usefulness in survival analysis of censored time-to-event data is yet to be verified. In this research, we investigate the plausibility of space extension technique, originally proposed for classification purpose, to survival analysis. By combing random subspace, bagging and extended space techniques, we develop a random survival forest with space extensions algorithm...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28606722/fully-automated-breast-boundary-and-pectoral-muscle-segmentation-in-mammograms
#15
Andrik Rampun, Philip J Morrow, Bryan W Scotney, John Winder
Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28602483/a-hybrid-framework-for-reverse-engineering-of-robust-gene-regulatory-networks
#16
Mina Jafari, Behnam Ghavami, Vahid Sattari
The inference of Gene Regulatory Networks (GRNs) using gene expression data in order to detect the basic cellular processes is a key issue in biological systems. Inferring GRN correctly requires inferring predictor set accurately. In this paper, a fast and accurate predictor set inference framework which linearly combines some inference methods is proposed. The purpose of the combination of various methods is to increase the accuracy of inferred GRN. The proposed framework offers a linear weighted combination of Pearson Correlation Coefficient (PCC) and two different feature selection approaches, namely: Information Gain (IG) and ReliefF...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28532962/from-snomed-ct-to-uberon-transferability-of-evaluation-methodology-between-similarly-structured-ontologies
#17
Gai Elhanan, Christopher Ochs, Jose L V Mejino, Hao Liu, Christopher J Mungall, Yehoshua Perl
OBJECTIVE: To examine whether disjoint partial-area taxonomy, a semantically-based evaluation methodology that has been successfully tested in SNOMED CT, will perform with similar effectiveness on Uberon, an anatomical ontology that belongs to a structurally similar family of ontologies as SNOMED CT. METHOD: A disjoint partial-area taxonomy was generated for Uberon. One hundred randomly selected test concepts that overlap between partial-areas were matched to a same size control sample of non-overlapping concepts...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28359635/protein-fold-recognition-based-on-sparse-representation-based-classification
#18
Ke Yan, Yong Xu, Xiaozhao Fang, Chunhou Zheng, Bin Liu
Knowledge of protein fold type is critical for determining the protein structure and function. Because of its importance, several computational methods for fold recognition have been proposed. Most of them are based on well-known machine learning techniques, such as Support Vector Machines (SVMs), Artificial Neural Network (ANN), etc. Although these machine learning methods play a role in stimulating the development of this important area, new techniques are still needed to further improve the predictive performance for fold recognition...
June 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28764874/machine-learning-based-identification-of-protein-protein-interactions-using-derived-features-of-physiochemical-properties-and-evolutionary-profiles
#19
Muhammad Tahir, Maqsood Hayat
Proteins are the central constitute of a cell or biological system. Proteins execute their functions by interacting with other molecules such as RNA, DNA and other proteins. The major functionality of protein-protein interactions (PPIs) is the execution of biochemical activities in living species. Therefore, an accurate identification of PPIs becomes a challenging and demanding task for investigators from last few decades. Various traditional and computational methods have been applied but they have not achieved quite encouraging results...
May 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28764873/automatic-detection-of-surgical-haemorrhage-using-computer-vision
#20
Alvaro Garcia-Martinez, Jose María Vicente-Samper, José María Sabater-Navarro
BACKGROUND AND OBJECTIVES: On occasions, a surgical intervention can be associated with serious, potentially life-threatening complications. One of these complications is a haemorrhage during the operation, an unsolved issue that could delay the intervention or even cause the patient's death. On laparoscopic surgery this complication is even more dangerous, due to the limited vision and mobility imposed by the minimally invasive techniques. METHODS: In this paper it is described a computer vision algorithm designed to analyse the images captured by a laparoscopic camera, classifying the pixels of each frame in blood pixels and background pixels and finally detecting a massive haemorrhage...
May 2017: Artificial Intelligence in Medicine
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