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Artificial Intelligence in Medicine

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
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
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
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
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
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
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
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
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
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
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
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
Leyi Wei, Shixiang Wan, Jiasheng Guo, Kelvin Kl Wong
Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization and hierarchical selection (PTHS). Our novel feature selection method is based on maximize the sum of relevance and distance (MSRD) for solving the problem of high dimensionality. Specifically, we have a PTHS algorithm that employs parallel optimization and candidate model pruning based on k-means and a hierarchical selection framework...
February 25, 2017: Artificial Intelligence in Medicine
Ruichu Cai, Mei Liu, Yong Hu, Brittany L Melton, Michael E Matheny, Hua Xu, Lian Duan, Lemuel R Waitman
OBJECTIVE: Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration's adverse event reporting system...
February 2017: Artificial Intelligence in Medicine
Luca Anselma, Luca Piovesan, Paolo Terenziani
BACKGROUND: Clinical practice guidelines (CPGs) are assuming a major role in the medical area, to grant the quality of medical assistance, supporting physicians with evidence-based information of interventions in the treatment of single pathologies. The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between CPGs...
February 2017: Artificial Intelligence in Medicine
Carlo Combi
No abstract text is available yet for this article.
February 2017: Artificial Intelligence in Medicine
Lei Chen, Yu-Hang Zhang, Guohui Lu, Tao Huang, Yu-Dong Cai
BACKGROUND: Cancer is a disease that involves abnormal cell growth and can invade or metastasize to other tissues. It is known that several factors are related to its initiation, proliferation, and invasiveness. Recently, it has been reported that long non-coding RNAs (lncRNAs) can participate in specific functional pathways and further regulate the biological function of cancer cells. Studies on lncRNAs are therefore helpful for uncovering the underlying mechanisms of cancer biological processes...
February 2017: Artificial Intelligence in Medicine
Ashkan Ebadi, Patrick J Tighe, Lei Zhang, Parisa Rashidi
OBJECTIVE: Surgical service providers play a crucial role in the healthcare system. Amongst all the influencing factors, surgical team selection might affect the patients' outcome significantly. The performance of a surgical team not only can depend on the individual members, but it can also depend on the synergy among team members, and could possibly influence patient outcome such as surgical complications. In this paper, we propose a tool for facilitating decision making in surgical team selection based on considering history of the surgical team, as well as the specific characteristics of each patient...
February 2017: Artificial Intelligence in Medicine
Zhen Li, Qianlan Yao, Songjian Zhao, Zhen Wang, Yixue Li
BACKGROUND: Esophageal adenocarcinoma (EAC) is one of the most aggressive gastroesophageal cancers. PTGS2, EGFR, ERBB2 and TP53 are the traditional EAC prognostic biomarkers, but they are still limited in their ability to effectively predict the overall survival. OBJECTIVES: To identify an improved biomarker for predicting the prognosis of EAC by using the expression profile. MATERIALS AND METHODS: Differential co-expression analysis and differential expression analysis were performed to identify the related genes of EAC...
February 2017: Artificial Intelligence in Medicine
Torgyn Shaikhina, Natalia A Khovanova
MOTIVATION: Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. METHODS: In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work...
January 2017: Artificial Intelligence in Medicine
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