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Artificial Intelligence in Medicine

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https://www.readbyqxmd.com/read/28733120/employing-decomposable-partially-observable-markov-decision-processes-to-control-gene-regulatory-networks
#1
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
#2
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
#3
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
#4
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/28701276/medical-image-classification-via-multiscale-representation-learning
#5
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 29, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28641924/random-survival-forest-with-space-extensions-for-censored-data
#6
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 19, 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
#7
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 17, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28662816/premature-ventricular-contraction-detection-combining-deep-neural-networks-and-rules-inference
#8
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 9, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28606722/fully-automated-breast-boundary-and-pectoral-muscle-segmentation-in-mammograms
#9
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 9, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28602483/a-hybrid-framework-for-reverse-engineering-of-robust-gene-regulatory-networks
#10
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 8, 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
#11
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
#12
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/28532962/from-snomed-ct-to-uberon-transferability-of-evaluation-methodology-between-similarly-structured-ontologies
#13
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...
May 19, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28456512/inter-labeler-and-intra-labeler-variability-of-condition-severity-classification-models-using-active-and-passive-learning-methods
#14
Nir Nissim, Yuval Shahar, Yuval Elovici, George Hripcsak, Robert Moskovitch
BACKGROUND AND OBJECTIVES: Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning...
April 26, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28410780/analyzing-interactions-on-combining-multiple-clinical-guidelines
#15
Veruska Zamborlini, Marcos da Silveira, Cedric Pruski, Annette Ten Teije, Edwin Geleijn, Marike van der Leeden, Martijn Stuiver, Frank van Harmelen
Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity...
April 11, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28377053/identification-of-transcription-factors-that-may-reprogram-lung-adenocarcinoma
#16
Chenglin Liu, Yu-Hang Zhang, Tao Huang, Yudong Cai
BACKGROUND: Lung adenocarcinoma is one of most threatening disease to human health. Although many efforts have been devoted to its genetic study, few researches have been focused on the transcription factors which regulate tumor initiation and progression by affecting multiple downstream gene transcription. It is proved that proper transcription factors may mediate the direct reprogramming of cancer cells, and reverse the tumorigenesis on the epigenetic and transcription levels. METHODS: In this paper, a computational method is proposed to identify the core transcription factors that can regulate as many as possible lung adenocarcinoma associated genes with as little as possible redundancy...
April 1, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28416144/feasibility-of-spirography-features-for-objective-assessment-of-motor-function-in-parkinson-s-disease
#17
Aleksander Sadikov, Vida Groznik, Martin Možina, Jure Žabkar, Dag Nyholm, Mevludin Memedi, Dejan Georgiev
OBJECTIVE: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations...
March 31, 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...
March 27, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28347562/automatic-segmentation-of-liver-tumors-from-multiphase-contrast-enhanced-ct-images-based-on-fcns
#19
Changjian Sun, Shuxu Guo, Huimao Zhang, Jing Li, Meimei Chen, Shuzhi Ma, Lanyi Jin, Xiaoming Liu, Xueyan Li, Xiaohua Qian
This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each phase of contrast-enhanced data provides distinct information on pathological features, we trained one network for each phase of the CT images and fused their high-layer features together. The proposed approach was validated on CT images taken from two databases: 3Dircadb and JDRD...
March 24, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28343742/automatic-matching-of-surgeries-to-predict-surgeons-next-actions
#20
Germain Forestier, François Petitjean, Laurent Riffaud, Pierre Jannin
OBJECTIVE: More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery. MATERIAL AND METHOD: We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities...
March 23, 2017: Artificial Intelligence in Medicine
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