An implicit approach to deal with periodically repeated medical data.

Abstract:

CONTEXT:Temporal information plays a crucial role in medicine, so that in medical informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit treatment is possible in most cases, it causes severe storage and disk I/O problems. OBJECTIVE:In this paper, we propose an innovative approach to cope with periodic relational medical data in an implicit way. METHODS:We propose a new data model, representing periodic data in a compact (implicit) way, which is a consistent extension of TSQL2 consensus approach. Then, we identify some important types of temporal queries, and present query answering algorithms to answer them. Finally, we also run experiments to evaluate our approach. RESULTS:The experiments show that our approach outperforms current explicit approaches, especially as regard disk I/O. CONCLUSION:We have provided an implicit approach to periodic data with is a consistent extension of TSQL2 (and which is thus grant interoperable with it), and we have experimentally proven that it outperforms current explicit approaches.

journal_name

Artif Intell Med

authors

Stantic B,Terenziani P,Governatori G,Bottrighi A,Sattar A

doi

10.1016/j.artmed.2012.03.002

subject

Has Abstract

pub_date

2012-07-01 00:00:00

pages

149-62

issue

3

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(12)00031-0

journal_volume

55

pub_type

杂志文章
  • On the use of pairwise distance learning for brain signal classification with limited observations.

    abstract::The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral pr...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101852

    authors: Calhas D,Romero E,Henriques R

    更新日期:2020-05-01 00:00:00

  • Detecting signals of detrimental prescribing cascades from social media.

    abstract:MOTIVATION:Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2016.06.002

    authors: Hoang T,Liu J,Pratt N,Zheng VW,Chang KC,Roughead E,Li J

    更新日期:2016-07-01 00:00:00

  • Recommendations for the ethical use and design of artificial intelligent care providers.

    abstract:OBJECTIVE:This paper identifies and reviews ethical issues associated with artificial intelligent care providers (AICPs) in mental health care and other helping professions. Specific recommendations are made for the development of ethical codes, guidelines, and the design of AICPs. METHODS:Current developments in the ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2014.06.004

    authors: Luxton DD

    更新日期:2014-09-01 00:00:00

  • Automatic processing of multilingual medical terminology: applications to thesaurus enrichment and cross-language information retrieval.

    abstract:OBJECTIVES:We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains. MATERIAL AND METHODS:We propose firstly a method for enriching mu...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2004.07.015

    authors: Déjean H,Gaussier E,Renders JM,Sadat F

    更新日期:2005-02-01 00:00:00

  • A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis.

    abstract::Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101950

    authors: Reyes O,Pérez E,Luque RM,Castaño J,Ventura S

    更新日期:2020-08-01 00:00:00

  • Evolving artificial neural networks for screening features from mammograms.

    abstract::Disagreement or inconsistencies in mammographic interpretation motivates utilizing computerized pattern recognition algorithms to aid the assessment of radiographic features. We have studied the potential for using artificial neural networks (ANNs) to analyze interpreted radiographic features from film screen mammogra...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(98)00040-2

    authors: Fogel DB,Wasson EC 3rd,Boughton EM,Porto VW

    更新日期:1998-11-01 00:00:00

  • Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

    abstract::Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101911

    authors: Lee WK,Wu CC,Lee CC,Lu CF,Yang HC,Huang TH,Lin CY,Chung WY,Wang PS,Wu HM,Guo WY,Wu YT

    更新日期:2020-07-01 00:00:00

  • Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI).

    abstract::Over the years, there has been growing interest in using machine learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc.) or the acquisition technology, which mi...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101804

    authors: Ferrari E,Retico A,Bacciu D

    更新日期:2020-03-01 00:00:00

  • Logarithmic simulated annealing for X-ray diagnosis.

    abstract::We present a new stochastic learning algorithm and first results of computational experiments on fragments of liver CT images. The algorithm is designed to compute a depth-three threshold circuit, where the first layer is calculated by an extension of the Perceptron algorithm by a special type of simulated annealing. ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(00)00112-3

    authors: Albrecht A,Steinhöfel K,Taupitz M,Wong CK

    更新日期:2001-06-01 00:00:00

  • An expert study evaluating the UMLS lexical metaschema.

    abstract:OBJECTIVE:A metaschema is an abstraction network of the UMLS's semantic network (SN) obtained from a connected partition of its collection of semantic types. A lexical metaschema was previously derived based on a lexical partition which partitioned the SN into semantic-type groups using identical word-usage among the n...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2005.01.002

    authors: Zhang L,Hripcsak G,Perl Y,Halper M,Geller J

    更新日期:2005-07-01 00:00:00

  • Terminological resources for text mining over biomedical scientific literature.

    abstract:OBJECTIVE:We present a combined terminological resource for text mining over biomedical literature. The purpose of the resource is to allow the detection of mentions of specific biological entities in scientific publications, and their grounding to widely accepted identifiers. This is an essential process, useful in it...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2011.04.011

    authors: Rinaldi F,Kaljurand K,Sætre R

    更新日期:2011-06-01 00:00:00

  • Using Arden Syntax for the creation of a multi-patient surveillance dashboard.

    abstract:OBJECTIVE:Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not native...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2015.09.009

    authors: Kraus S,Drescher C,Sedlmayr M,Castellanos I,Prokosch HU,Toddenroth D

    更新日期:2018-11-01 00:00:00

  • Identification of sympathetic and parasympathetic nerves function in cardiovascular regulation using ANFIS approximation.

    abstract:OBJECTIVE:In this paper a new nonlinear system identification approach is developed for dynamical quantification of cardiovascular regulation. This approach is specifically focused on the identification of the heart rate (HR) baroreflex mechanism. The principal objective of this paper is to improve the model accuracy i...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2011.01.002

    authors: Jalali A,Ghaffari A,Ghorbanian P,Nataraj C

    更新日期:2011-05-01 00:00:00

  • Bridge and brick network motifs: identifying significant building blocks from complex biological systems.

    abstract:OBJECTIVE:A major focus in computational system biology research is defining organizing principles that govern complex biological network formation and evolution. The task is considered a major challenge because network behavior and function prediction requires the identification of functionally and statistically impor...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2007.07.006

    authors: Huang CY,Cheng CY,Sun CT

    更新日期:2007-10-01 00:00:00

  • Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors.

    abstract:OBJECTIVE:We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor classifiers. METHODS AND MATERIAL:Gene expression based cancer classificat...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2008.08.004

    authors: Okun O,Priisalu H

    更新日期:2009-02-01 00:00:00

  • Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis.

    abstract::Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101927

    authors: Xie J,Jiang J,Wang Y,Guan Y,Guo X

    更新日期:2020-07-01 00:00:00

  • Information extraction from multi-institutional radiology reports.

    abstract:OBJECTIVES:The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinic...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章,多中心研究

    doi:10.1016/j.artmed.2015.09.007

    authors: Hassanpour S,Langlotz CP

    更新日期:2016-01-01 00:00:00

  • Comparative study of approximate entropy and sample entropy robustness to spikes.

    abstract:OBJECTIVE:There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the influence of abnormal spikes ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2011.06.007

    authors: Molina-Picó A,Cuesta-Frau D,Aboy M,Crespo C,Miró-Martínez P,Oltra-Crespo S

    更新日期:2011-10-01 00:00:00

  • Cancer survival classification using integrated data sets and intermediate information.

    abstract:OBJECTIVE:Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2014.06.003

    authors: Kim S,Park T,Kon M

    更新日期:2014-09-01 00:00:00

  • Case-based reasoning for medical decision support tasks: the Inreca approach.

    abstract::We describe an approach for developing knowledge-based medical decision support systems based on the new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca + project which mainly deals with medical applications. One goal was to s...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(97)00038-9

    authors: Althoff KD,Bergmann R,Wess S,Manago M,Auriol E,Larichev OI,Bolotov A,Zhuravlev YI,Gurov SI

    更新日期:1998-01-01 00:00:00

  • ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network.

    abstract::Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method fo...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101856

    authors: Zhang J,Liu A,Gao M,Chen X,Zhang X,Chen X

    更新日期:2020-06-01 00:00:00

  • Exploring ant-based algorithms for gene expression data analysis.

    abstract:OBJECTIVE:Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2009.03.004

    authors: He Y,Hui SC

    更新日期:2009-10-01 00:00:00

  • Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text.

    abstract::Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting kn...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2004.01.015

    authors: Cao C,Wang H,Sui Y

    更新日期:2004-09-01 00:00:00

  • Evaluation of two different models of semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA.

    abstract::As part of a plan to promote semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA, this study sought to explore and cope with the variability of results that may be anticipated when performing knowledge acquisition with patient data from different patient settings. Patient data were ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(02)00025-8

    authors: Leitich H,Adlassnig KP,Kolarz G

    更新日期:2002-07-01 00:00:00

  • iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

    abstract::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 peri...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.06.008

    authors: Akbar S,Hayat M,Iqbal M,Jan MA

    更新日期:2017-06-01 00:00:00

  • Component-based mediation services for the integration of medical applications.

    abstract::Allowing exchange of information and cooperation among network-wide distributed and heterogeneous applications is a major need of current health-care information systems. The European project SynEx aims at developing an integration platform for both new and legacy applications on each partner's site. We developed, in ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(03)00007-1

    authors: Xu Y,Sauquet D,Degoulet P,Jaulent MC

    更新日期:2003-03-01 00:00:00

  • Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection.

    abstract::Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilaye...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(03)00053-8

    authors: Antal P,Fannes G,Timmerman D,Moreau Y,De Moor B

    更新日期:2003-09-01 00:00:00

  • Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs.

    abstract:OBJECTIVE:We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS:Both time and f...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2016.01.005

    authors: Yin XX,Hadjiloucas S,Zhang Y,Su MY,Miao Y,Abbott D

    更新日期:2016-02-01 00:00:00

  • Analyzing interactions on combining multiple clinical guidelines.

    abstract::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 a...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.03.012

    authors: Zamborlini V,da Silveira M,Pruski C,Ten Teije A,Geleijn E,van der Leeden M,Stuiver M,van Harmelen F

    更新日期:2017-09-01 00:00:00

  • Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

    abstract:OBJECTIVE:In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2012.12.003

    authors: Bennett CC,Hauser K

    更新日期:2013-01-01 00:00:00