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Artificial Intelligence in Medicine

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https://www.readbyqxmd.com/read/27964803/web-video-mining-supported-workflow-modeling-for-laparoscopic-surgeries
#1
Rui Liu, Xiaoli Zhang, Hao Zhang
MOTIVATION: As quality assurance is of strong concern in advanced surgeries, intelligent surgical systems are expected to have knowledge such as the knowledge of the surgical workflow model (SWM) to support their intuitive cooperation with surgeons. For generating a robust and reliable SWM, a large amount of training data is required. However, training data collected by physically recording surgery operations is often limited and data collection is time-consuming and labor-intensive, severely influencing knowledge scalability of the surgical systems...
November 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27964802/analysis-of-correlation-between-pediatric-asthma-exacerbation-and-exposure-to-pollutant-mixtures-with-association-rule-mining
#2
Giulia Toti, Ricardo Vilalta, Peggy Lindner, Barry Lefer, Charles Macias, Daniel Price
OBJECTIVES: Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures...
November 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27964801/gesteme-free-context-aware-adaptation-of-robot-behavior-in-human-robot-cooperation
#3
Federico Nessi, Elisa Beretta, Cecilia Gatti, Giancarlo Ferrigno, Elena De Momi
BACKGROUND: Cooperative robotics is receiving greater acceptance because the typical advantages provided by manipulators are combined with an intuitive usage. In particular, hands-on robotics may benefit from the adaptation of the assistant behavior with respect to the activity currently performed by the user. A fast and reliable classification of human activities is required, as well as strategies to smoothly modify the control of the manipulator. In this scenario, gesteme-based motion classification is inadequate because it needs the observation of a wide signal percentage and the definition of a rich vocabulary...
November 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27964800/an-adaptive-large-neighborhood-search-procedure-applied-to-the-dynamic-patient-admission-scheduling-problem
#4
Richard Martin Lusby, Martin Schwierz, Troels Martin Range, Jesper Larsen
OBJECTIVE: The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting patient comfort and treatment efficiency are maximized. Consideration must be given to uncertainty in the length of stays of patients as well as the possibility of emergency patients...
November 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27964799/non-obvious-correlations-to-disease-management-unraveled-by-bayesian-artificial-intelligence-analyses-of-cms-data
#5
Vijetha Vemulapalli, Jiaqi Qu, Jeonifer M Garren, Leonardo O Rodrigues, Michael A Kiebish, Rangaprasad Sarangarajan, Niven R Narain, Viatcheslav R Akmaev
OBJECTIVE: Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions. MATERIALS AND METHODS: Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology...
November 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27926382/prediction-of-anti-cancer-drug-response-by-kernelized-multi-task-learning
#6
Mehmet Tan
MOTIVATION: Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim...
October 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27926381/a-review-on-brain-structures-segmentation-in-magnetic-resonance-imaging
#7
REVIEW
Sandra González-Villà, Arnau Oliver, Sergi Valverde, Liping Wang, Reyer Zwiggelaar, Xavier Lladó
BACKGROUND AND OBJECTIVES: Automatic brain structures segmentation in magnetic resonance images has been widely investigated in recent years with the goal of helping diagnosis and patient follow-up in different brain diseases. Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches. METHODS: We divide first the algorithms according to their target structures and then we propose a general classification based on their segmentation strategy, which includes atlas-based, learning-based, deformable, region-based and hybrid methods...
October 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27926380/out-of-hours-workload-management-bayesian-inference-for-decision-support-in-secondary-care
#8
Iker Perez, Michael Brown, James Pinchin, Sarah Martindale, Sarah Sharples, Dominick Shaw, John Blakey
OBJECTIVE: In this paper, we aim to evaluate the use of electronic technologies in out of hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. METHODS AND MATERIAL: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom...
October 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27926379/a-comparative-analysis-of-chaotic-particle-swarm-optimizations-for-detecting-single-nucleotide-polymorphism-barcodes
#9
Li-Yeh Chuang, Sin-Hua Moi, Yu-Da Lin, Cheng-Hong Yang
OBJECTIVE: Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset...
October 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27926378/automated-segmentation-of-white-matter-fiber-bundles-using-diffusion-tensor-imaging-data-and-a-new-density-based-clustering-algorithm
#10
Tahereh Kamali, Daniel Stashuk
OBJECTIVE: Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications...
October 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27926377/brain-tumor-segmentation-from-multimodal-magnetic-resonance-images-via-sparse-representation
#11
Yuhong Li, Fucang Jia, Jing Qin
OBJECTIVE: Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. METHODS: We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability...
October 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27664509/a-mixed-ensemble-model-for-hospital-readmission
#12
Lior Turgeman, Jerrold H May
OBJECTIVE: A hospital readmission is defined as an admission to a hospital within a certain time frame, typically thirty days, following a previous discharge, either to the same or to a different hospital. Because most patients are not readmitted, the readmission classification problem is highly imbalanced. MATERIALS AND METHODS: We developed a hospital readmission predictive model, which enables controlling the tradeoff between reasoning transparency and predictive accuracy, by taking into account the unique characteristics of the learned database...
September 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27664508/efficient-processing-of-multiple-nested-event-pattern-queries-over-multi-dimensional-event-streams-based-on-a-triaxial-hierarchical-model
#13
Fuyuan Xiao, Masayoshi Aritsugi, Qing Wang, Rong Zhang
OBJECTIVE: For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper. METHODS AND MATERIAL: Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively...
September 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27664507/prediction-of-lung-cancer-incidence-on-the-low-dose-computed-tomography-arm-of-the-national-lung-screening-trial-a-dynamic-bayesian-network
#14
Panayiotis Petousis, Simon X Han, Denise Aberle, Alex A T Bui
INTRODUCTION: Identifying high-risk lung cancer individuals at an early disease stage is the most effective way of improving survival. The landmark National Lung Screening Trial (NLST) demonstrated the utility of low-dose computed tomography (LDCT) imaging to reduce mortality (relative to X-ray screening). As a result of the NLST and other studies, imaging-based lung cancer screening programs are now being implemented. However, LDCT interpretation results in a high number of false positives...
September 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27664506/meta-glare-a-meta-system-for-defining-your-own-computer-interpretable-guideline-system-architecture-and-acquisition
#15
Alessio Bottrighi, Paolo Terenziani
CONTEXT: Several different computer-assisted management systems of computer interpretable guidelines (CIGs) have been developed by the Artificial Intelligence in Medicine community. Each CIG system is characterized by a specific formalism to represent CIGs, and usually provides a manager to acquire, consult and execute them. Though there are several commonalities between most formalisms in the literature, each formalism has its own peculiarities. OBJECTIVE: The goal of our work is to provide a flexible support to the extension or definition of CIGs formalisms, and of their acquisition and execution engines...
September 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27664505/building-interpretable-predictive-models-for-pediatric-hospital-readmission-using-tree-lasso-logistic-regression
#16
Milos Jovanovic, Sandro Radovanovic, Milan Vukicevic, Sven Van Poucke, Boris Delibasic
OBJECTIVES: Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights...
September 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27664504/survival-analysis-for-high-dimensional-heterogeneous-medical-data-exploring-feature-extraction-as-an-alternative-to-feature-selection
#17
Sebastian Pölsterl, Sailesh Conjeti, Nassir Navab, Amin Katouzian
BACKGROUND: In clinical research, the primary interest is often the time until occurrence of an adverse event, i.e., survival analysis. Its application to electronic health records is challenging for two main reasons: (1) patient records are comprised of high-dimensional feature vectors, and (2) feature vectors are a mix of categorical and real-valued features, which implies varying statistical properties among features. To learn from high-dimensional data, researchers can choose from a wide range of methods in the fields of feature selection and feature extraction...
September 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27686851/executable-medical-guidelines-with-arden-syntax-applications-in-dermatology-and-obstetrics
#18
Alexander Seitinger, Andrea Rappelsberger, Harald Leitich, Michael Binder, Klaus-Peter Adlassnig
INTRODUCTION: Clinical decision support systems (CDSSs) are being developed to assist physicians in processing extensive data and new knowledge based on recent scientific advances. Structured medical knowledge in the form of clinical alerts or reminder rules, decision trees or tables, clinical protocols or practice guidelines, score algorithms, and others, constitute the core of CDSSs. Several medical knowledge representation and guideline languages have been developed for the formal computerized definition of such knowledge...
August 12, 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27773563/evolution-of-the-arden-syntax-key-technical-issues-from-the-standards-development-organization-perspective
#19
Robert A Jenders, Klaus-Peter Adlassnig, Karsten Fehre, Peter Haug
BACKGROUND: The initial version of the Arden Syntax for Medical Logic Systems was created to facilitate explicit representation of medical logic in a form that could be easily composed and interpreted by clinical experts in order to facilitate clinical decision support (CDS). Because of demand from knowledge engineers and programmers to improve functionality related to complex use cases, the Arden Syntax evolved to include features typical of general programming languages but that were specialized to meet the needs of the clinical decision support environment, including integration into a clinical information system architecture...
August 11, 2016: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/27521266/corrigendum-to-defocus-aware-dirichlet-particle-filter-for-stable-endoscopic-video-frame-recognition-artif-intell-med-68-march-2016-1-16
#20
Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Yoko Kominami, Shinji Tanaka
No abstract text is available yet for this article.
August 9, 2016: Artificial Intelligence in Medicine
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