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Artificial Intelligence in Medicine

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https://www.readbyqxmd.com/read/28641924/random-survival-forest-with-space-extensions-for-censored-data
#1
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/28606722/fully-automated-breast-boundary-and-pectoral-muscle-segmentation-in-mammograms
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
https://www.readbyqxmd.com/read/28325605/user-recommendation-in-healthcare-social-media-by-assessing-user-similarity-in-heterogeneous-network
#14
Ling Jiang, Christopher C Yang
OBJECTIVE: The rapid growth of online health social websites has captured a vast amount of healthcare information and made the information easy to access for health consumers. E-patients often use these social websites for informational and emotional support. However, health consumers could be easily overwhelmed by the overloaded information. Healthcare information searching can be very difficult for consumers, not to mention most of them are not skilled information searcher. In this work, we investigate the approaches for measuring user similarity in online health social websites...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#15
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28283358/identify-and-analysis-crotonylation-sites-in-histone-by-using-support-vector-machines
#16
Wang-Ren Qiu, Bi-Qian Sun, Hua Tang, Jian Huang, Hao Lin
OBJECTIVE: Lysine crotonylation (Kcr) is a newly discovered histone posttranslational modification, which is specifically enriched at active gene promoters and potential enhancers in mammalian cell genomes. Although lysine crotonylation sites can be correctly identified with high-resolution mass spectrometry, the experimental methods are time-consuming and expensive. Therefore, it is necessary to develop computational methods to deal with this problem. METHODS: We proposed a new encoding scheme named position weight amino acid composition to extract sequence information of histone around crotonylation sites...
March 7, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28320624/improved-prediction-of-protein-protein-interactions-using-novel-negative-samples-features-and-an-ensemble-classifier
#17
Leyi Wei, Pengwei Xing, Jiancang Zeng, JinXiu Chen, Ran Su, Fei Guo
Computational methods are employed in bioinformatics to predict protein-protein interactions (PPIs). PPIs and protein-protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that of PPNIs. This significant difference in the number of PPIs and PPNIs increases the cost of constructing a balanced dataset. PPIs can be classified as either physical or genetic. However, ready-made PPNI databases were proven only to have no physical interactions and were not proven to have no genetic interactions...
March 4, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28545612/drug-repositioning-based-on-triangularly-balanced-structure-for-tissue-specific-diseases-in-incomplete-interactome
#18
Liang Yu, Jin Zhao, Lin Gao
Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue specificities of diseases. Moreover, disease genes, drug targets and protein interaction (PPI) networks remain largely incomplete and the relationships between drugs and diseases conform to the triangularly balanced structure. Therefore, based on tissue specificities of diseases, we apply the triangularly balanced theory and the module distance defined for incomplete interaction networks to build drug-disease associations...
March 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28545611/knowledge-graph-for-tcm-health-preservation-design-construction-and-applications
#19
Tong Yu, Jinghua Li, Qi Yu, Ye Tian, Xiaofeng Shun, Lili Xu, Ling Zhu, Hongjie Gao
Traditional Chinese Medicine (TCM) is one of the important non-material cultural heritages of the Chinese nation. It is an important development strategy of Chinese medicine to collect, analyzes, and manages the knowledge assets of TCM health care. As a novel and massive knowledge management technology, knowledge graph provides an ideal technical means to solve the problem of "Knowledge Island" in the field of traditional Chinese medicine. In this study, we construct a large-scale knowledge graph, which integrates terms, documents, databases and other knowledge resources...
March 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28545610/a-case-based-reasoning-system-based-on-weighted-heterogeneous-value-distance-metric-for-breast-cancer-diagnosis
#20
Dongxiao Gu, Changyong Liang, Huimin Zhao
OBJECTIVE: We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. METHODS: We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts...
March 2017: Artificial Intelligence in Medicine
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