Abstract：
:We introduce a novel approach to couple temporal similarity with spatial neighborhood information. This is achieved by concatenating the K nearest, spatially contiguous neighbors of a pixel time-course (TC) of T time-instances. This produces a new TC of (K+1)T time instances. Depending on how "nearest" is defined, we have various options. Strictly spatial nearness means augmenting a given TC by its K nearest neighbors in some canonical spatial order. A more powerful and flexible option is to order the TCs to be concatenated according to their temporal similarity to the central voxel TC. For this study, we have chosen Pearson's cross-correlation coefficient as the measure of similarity. For more than a single neighbor, two concatenation options are possible. The direct ordering option requires that the TCs to be concatenated be spatially contiguous to the central pixel. The more flexible indirect option merely demands that one of a chain of temporally similar TCs be spatially connected to the central pixel. We also apply the temporal similarity criterion to the more conventional spatial (median) filtering, and show that it gives superior result to a strict spatial filtering. The method is tested and verified on a null fMRI dataset onto which we superposed two types of "activations" with known temporal behavior and spatial location. It is also applied to a real dataset containing visual activation. We also propose a strategy, based on the flexibility of the method, to determine a consensus, "core" set of activations.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Somorjai RL,Vivanco R,Pizzi Ndoi
10.1016/s0933-3657(02)00005-2subject
Has Abstractpub_date
2002-05-01 00:00:00pages
5-17issue
1eissn
0933-3657issn
1873-2860pii
S0933365702000052journal_volume
25pub_type
杂志文章abstract：:Medical language is highly compositional and makes extensive use of common roots, especially Latino-Greek roots. Besides words devoted to common sense, medical language presents some typical characteristics, especially on morphological and semantic aspects of word formation. Morphological decomposition and identificat...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00023-2
更新日期：1998-09-01 00:00:00
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
更新日期：2013-01-01 00:00:00
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
更新日期：2020-01-01 00:00:00
abstract：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...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.10.002
更新日期：2016-11-01 00:00:00
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
更新日期：2020-07-01 00:00:00
abstract：:Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particl...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101790
更新日期：2020-04-01 00:00:00
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
更新日期：2018-04-01 00:00:00
abstract：:Abnormalities in the organization of brain circuits may underlie many types of epilepsy. This hypothesis can best be evaluated in the case of temporal lobe epilepsy, where evidence of rewiring (synaptic reorganization) can be found in the dentate gyrus. Computer modeling of normal and reorganized dentate gyrus was use...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00005-0
更新日期：1998-05-01 00:00:00
abstract：OBJECTIVES:We propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of nasopharyngeal carcinoma (NPC) on a case-control epidemiologic study that consists of 1289 subjects and 150 risk factors. METHODS:This framework build...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.09.002
更新日期：2012-01-01 00:00:00
abstract：OBJECTIVE:Arden Syntax is a standard for representing and sharing medical knowledge in form of independent modules and looks back on a history of 25 years. Its traditional field of application is the monitoring of clinical events such as generating an alert in case of occurrence of a critical laboratory result. Arden S...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2015.09.003
更新日期：2018-11-01 00:00:00
abstract：:This paper presents a computer-based model for differential diagnosis of specific language impairment (SLI), a language disorder that, in many cases, cannot be easily diagnosed. This difficulty necessitates the development of a methodology to assist the speech therapist in the diagnostic process. The methodology tool ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00076-3
更新日期：2003-11-01 00:00:00
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
更新日期：2013-05-01 00:00:00
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
更新日期：2005-06-01 00:00:00
abstract：:In this contribution we present an application of a knowledge-based neural network technique in the domain of medical research. We consider the crucial problem of intensive care patients developing a septic shock during their stay at the intensive care unit. Septic shock is of prime importance in intensive care medici...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(03)00057-5
更新日期：2003-06-01 00:00:00
abstract：:Within the framework of the OPTIVIP project, an optic nerve based visual prosthesis is developed in order to restore partial vision to the blind. One of the main challenges is to understand, decode and model the physiological process linking the stimulating parameters to the visual sensations produced in the visual fi...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.02.004
更新日期：2004-11-01 00:00:00
abstract：OBJECTIVE:Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2009.07.012
更新日期：2010-02-01 00:00:00
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
更新日期：2009-02-01 00:00:00
abstract：OBJECTIVE:This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation i...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.12.006
更新日期：2014-03-01 00:00:00
abstract：OBJECTIVE:This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. METHODS AND MATERIAL:Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an ey...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.04.005
更新日期：2008-07-01 00:00:00
abstract：AIM:A new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101816
更新日期：2020-03-01 00:00:00
abstract：MOTIVATIONS:It has recently been argued [1] that the effectiveness of a cure depends on the doctor-patient shared understanding of an illness and its treatment. Although a better communication between doctor and patient can be pursued through dedicated training programs, or by collecting patients' experiences and sympt...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.101727
更新日期：2019-11-01 00:00:00
abstract：:We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed,...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(01)00101-4
更新日期：2002-02-01 00:00:00
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
更新日期：2020-07-01 00:00:00
abstract：:During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2018.02.001
更新日期：2018-04-01 00:00:00
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
更新日期：2001-06-01 00:00:00
abstract：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...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.09.005
更新日期：2016-10-01 00:00:00
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
更新日期：2000-10-01 00:00:00
abstract：BACKGROUND:After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more inform...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.01.001
更新日期：2016-02-01 00:00:00
abstract：OBJECTIVE:The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing int...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2006.08.005
更新日期：2007-02-01 00:00:00
abstract：OBJECTIVE:This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multi...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.02.002
更新日期：2013-05-01 00:00:00
