当前位置： SCI文献检索 > ARTIFICIAL INTELLIGENCE IN MEDICINE期刊下所有文献 > The determination of three subcutaneous adipose tissue compartments in non-insulin-dependent diabetes mellitus women with artificial neural networks and factor analysis.

The determination of three subcutaneous adipose tissue compartments in non-insulin-dependent diabetes mellitus women with artificial neural networks and factor analysis.

Abstract：

:The optical device LIPOMETER allows for non-invasive, quick, precise and safe determination of subcutaneous fat distribution, so-called subcutaneous adipose tissue topography (SAT-Top). In this paper, we show how the high-dimensional SAT-Top information of women with type-2 diabetes mellitus (non-insulin-dependent diabetes mellitus (NIDDM)) and a healthy control group can be analysed and represented in low-dimensional plots by applying factor analysis and special artificial neural networks. Three top-down sorted subcutaneous adipose tissue compartments are determined (upper trunk, lower trunk, legs). NIDDM women provide significantly higher upper trunk obesity and significantly lower leg obesity ('apple' type), as compared with their healthy control group. Further, we show that the results of the applied networks are very similar to the results of factor analysis.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Tafeit E,Möller R,Sudi K,Reibnegger G

doi

10.1016/s0933-3657(99)00017-2

subject

Has Abstract

pub_date

1999-10-01 00:00:00

pages

181-93

issue

2

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(99)00017-2

journal_volume

17

pub_type

临床试验,杂志文章

相关文献

ARTIFICIAL INTELLIGENCE IN MEDICINE文献大全
  • Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation.

    abstract：OBJECTIVE:In this paper, we propose a novel algorithm to extract the active intervals of intracardiac electrograms during atrial fibrillation. METHODS:First, we show that the characteristics of the signal waveform at its inflection points are prominent features that are implicitly used by human annotators for distingu...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2018.02.003

    authors： Hajimolahoseini H,Hashemi J,Gazor S,Redfearn D

    更新日期：2018-04-01 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

  • Continual planning and scheduling for managing patient tests in hospital laboratories.

    abstract：:Hospital laboratories perform examination tests upon patients, in order to assist medical diagnosis or therapy progress. Planning and scheduling patient requests for examination tests is a complicated problem because it concerns both minimization of patient stay in hospital and maximization of laboratory resources uti...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(00)00061-0

    authors： Marinagi CC,Spyropoulos CD,Papatheodorou C,Kokkotos S

    更新日期：2000-10-01 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

  • Transferring brain-computer interfaces beyond the laboratory: successful application control for motor-disabled users.

    abstract：OBJECTIVES:Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI experts around. But are the technology and the field mature enough f...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2013.08.004

    authors： Leeb R,Perdikis S,Tonin L,Biasiucci A,Tavella M,Creatura M,Molina A,Al-Khodairy A,Carlson T,Millán JD

    更新日期：2013-10-01 00:00:00

  • Patterns for collaborative work in health care teams.

    abstract：OBJECTIVE:The problem of designing and managing teams of workers that can collaborate working together towards common goals is a challenging one. Incomplete or ambiguous specification of responsibilities and accountabilities, lack of continuity in teams working in shifts, inefficient organization of teams due to lack o...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.08.005

    authors： Grando MA,Peleg M,Cuggia M,Glasspool D

    更新日期：2011-11-01 00:00:00

  • Selection of patients for clinical trials: an interactive web-based system.

    abstract：:The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual pro...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.01.017

    authors： Fink E,Kokku PK,Nikiforou S,Hall LO,Goldgof DB,Krischer JP

    更新日期：2004-07-01 00:00:00

  • Neuro-genetic non-invasive temperature estimation: intensity and spatial prediction.

    abstract：OBJECTIVES:The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In th...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2008.03.008

    authors： Teixeira CA,Ruano MG,Ruano AE,Pereira WC

    更新日期：2008-06-01 00:00:00

  • Anatomical sketch understanding: recognizing explicit and implicit structure.

    abstract：OBJECTIVE:Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applicat...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2006.07.010

    authors： Haddawy P,Dailey MN,Kaewruen P,Sarakhette N,Hai le H

    更新日期：2007-02-01 00:00:00

  • Modeling and solving the dynamic patient admission scheduling problem under uncertainty.

    abstract：OBJECTIVE:Our goal is to propose and solve a new formulation of the recently-formalized patient admission scheduling problem, extending it by including several real-world features, such as the presence of emergency patients, uncertainty in the length of stay, and the possibility of delayed admissions. METHOD:We devise...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2012.09.001

    authors： Ceschia S,Schaerf A

    更新日期：2012-11-01 00:00:00

  • 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

  • Argumentation-logic for creating and explaining medical hypotheses.

    abstract：OBJECTIVE:While EIRA has proved to be successful in the detection of anomalous patient responses to treatments in the Intensive Care Unit, it could not describe to clinicians the rationales behind the anomalous detections. The aim of this paper is to address this problem. METHODS:Few attempts have been made in the pas...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2013.02.003

    authors： Grando MA,Moss L,Sleeman D,Kinsella J

    更新日期：2013-05-01 00:00:00

  • Temporal similarity measures for querying clinical workflows.

    abstract：OBJECTIVE:In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal concept...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2008.07.013

    authors： Combi C,Gozzi M,Oliboni B,Juarez JM,Marin R

    更新日期：2009-05-01 00:00:00

  • Topic-informed neural approach for biomedical event extraction.

    abstract：:As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.101783

    authors： Zhang J,Liu M,Zhang Y

    更新日期：2020-03-01 00:00:00

  • A spatio-temporal Bayesian network classifier for understanding visual field deterioration.

    abstract：OBJECTIVE:Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.07.004

    authors： Tucker A,Vinciotti V,Liu X,Garway-Heath D

    更新日期：2005-06-01 00:00:00

  • Predicting ICU readmission using grouped physiological and medication trends.

    abstract：BACKGROUND:Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmi...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2018.08.004

    authors： Xue Y,Klabjan D,Luo Y

    更新日期：2019-04-01 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

  • 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

  • Finding temporal patterns--a set-based approach.

    abstract：:We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relationships among sets of ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/0933-3657(94)90066-3

    authors： Wade TD,Byrns PJ,Steiner JF,Bondy J

    更新日期：1994-06-01 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

  • Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods.

    abstract：:Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progr...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.101742

    authors： Zahia S,Garcia Zapirain MB,Sevillano X,González A,Kim PJ,Elmaghraby A

    更新日期：2020-01-01 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

  • HepatoConsult: a knowledge-based second opinion and documentation system.

    abstract：:HepatoConsult is a publicly available knowledge-based second opinion and documentation system aiding in the diagnosis of liver diseases. The positive results of a prospective diagnostic evaluation study encouraged its use in clinical routine, although the available hardware infrastructure was not optimal. The comments...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(01)00104-x

    authors： Buscher HP,Engler Ch,Führer A,Kirschke S,Puppe F

    更新日期：2002-03-01 00:00:00

  • Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

    abstract：:The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive an...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.02.002

    authors： Ghosh S,Chattopadhyay BP,Roy RM,Mukherjee J,Mahadevappa M

    更新日期：2019-05-01 00:00:00

  • A novel deep mining model for effective knowledge discovery from omics data.

    abstract：:Knowledge discovery from omics data has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, high-throughput biomedical datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101821

    authors： Alzubaidi A,Tepper J,Lotfi A

    更新日期：2020-04-01 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

  • Specification of models in large expert systems based on causal probabilistic networks.

    abstract：:Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between node...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/0933-3657(93)90029-3

    authors： Olesen KG,Andreassen S

    更新日期：1993-06-01 00:00:00

  • Retrieving cases for treatment advice in nursing using text representation and structured text retrieval.

    abstract：:A nursing database which records patient details and treatments as fields in a standard database format is transformed into a collection, in text form, of patient case days with history. Each case is represented as text strings encoding the patient details, the current problems, treatments and their associated history...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(96)00362-4

    authors： Yearwood J,Wilkinson R

    更新日期：1997-01-01 00:00:00

  • Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

    abstract：OBJECTIVES:The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system. METHOD:Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorit...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.06.005

    authors： Alexiadis A

    更新日期：2019-07-01 00:00:00

  • Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

    abstract：:In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supportin...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101931

    authors： Van Woensel W,Roy PC,Abidi SSR,Abidi SR

    更新日期：2020-08-01 00:00:00