Finding temporal patterns--a set-based approach.

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

authors： Aliaga I,Vera V,Vera M,García E,Pedrera M,Pajares G

更新日期：2020-03-01 00:00:00

abstract：OBJECTIVE:One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher ...

journal_title：Artificial intelligence in medicine

authors： Chella A,Frixione M,Gaglio S

更新日期：2008-10-01 00:00:00

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

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

更新日期：1998-11-01 00:00:00

abstract：:In this paper, we propose an approach for managing clinical guidelines. We outline a modular architecture, allowing us to separate two conceptually distinct aspects: the representation (and acquisition) of clinical guidelines and their execution. We propose an expressive formalism, which allows one to deal with the co...

journal_title：Artificial intelligence in medicine

authors： Terenziani P,Molino G,Torchio M

更新日期：2001-11-01 00:00:00

abstract：BACKGROUND AND MOTIVATION:DNA microarray technology has made it possible to determine the expression levels of thousands of genes in parallel under multiple experimental conditions. Genome-wide analyses using DNA microarrays make a great contribution to the exploration of the dynamic state of genetic networks, and furt...

journal_title：Artificial intelligence in medicine

authors： Okada Y,Sahara T,Mitsubayashi H,Ohgiya S,Nagashima T

更新日期：2005-09-01 00:00:00

abstract：:A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a finite number of hidden unit activation...

journal_title：Artificial intelligence in medicine

authors： Setiono R

更新日期：1996-02-01 00:00:00

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

authors： Zhang J,Liu M,Zhang Y

更新日期：2020-03-01 00:00:00

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

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

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

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

更新日期：2020-07-01 00:00:00

abstract：:Standard Back Propagation (BP), Partially Recurrent (PR) and Cascade-Correlation (CC) neural networks were used to predict the side of finger movement on the basis of non-averaged single trial multi-channel EEG data recorded prior to movement. From these EEG data, power values were calculated and used as parameters fo...

journal_title：Artificial intelligence in medicine

authors： Masic N,Pfurtscheller G

更新日期：1993-12-01 00:00:00

abstract：:In this paper, we describe a system for automating the diagnosis of myocardial perfusion from single-photon emission computerized tomography (SPECT) images of male and female hearts. Initially we had several thousand of SPECT images, other clinical data and physician-interpreter's descriptions of the images. The image...

journal_title：Artificial intelligence in medicine

authors： Sacha JP,Goodenday LS,Cios KJ

更新日期：2002-09-01 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

authors： Lytton WW,Hellman KM,Sutula TP

更新日期：1998-05-01 00:00:00

abstract：OBJECTIVE:The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles. METHODS AND MATERIALS:All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored b...

journal_title：Artificial intelligence in medicine

authors： Huupponen E,Gómez-Herrero G,Saastamoinen A,Värri A,Hasan J,Himanen SL

更新日期：2007-07-01 00:00:00

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 ...

journal_title：Artificial intelligence in medicine

authors： Somorjai RL,Vivanco R,Pizzi N

更新日期：2002-05-01 00:00:00

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

authors： Xue Y,Klabjan D,Luo Y

更新日期：2019-04-01 00:00:00

abstract：OBJECTIVE:The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords. METHODOLOGY:The problem is treated as a pattern recognition task. To obtain a concise and informative re...

journal_title：Artificial intelligence in medicine

authors： Verikas A,Gelzinis A,Bacauskiene M,Uloza V

更新日期：2006-01-01 00:00:00

abstract：:A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an incr...

journal_title：Artificial intelligence in medicine

authors： Al-Shebani Q,Premaratne P,McAndrew DJ,Vial PJ,Abey S

更新日期：2019-03-01 00:00:00

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

authors： Alexiadis A

更新日期：2019-07-01 00:00:00

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

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

更新日期：2007-02-01 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

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

更新日期：2013-05-01 00:00:00

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

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

abstract：:Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer p...

journal_title：Artificial intelligence in medicine

authors： Spänig S,Emberger-Klein A,Sowa JP,Canbay A,Menrad K,Heider D

更新日期：2019-09-01 00:00:00

abstract：:If our goal in Artificial Intelligence in Medicine (AIM) is to engineer systems health-care providers will both use and, in the process, improve their performance, we must concentrate on the development of causal theories of knowledge and problem solving. One broad direction in pursuing this goal is understanding the ...

journal_title：Artificial intelligence in medicine

authors： Smith JW Jr,Bayazitoglu A

更新日期：1993-04-01 00:00:00

abstract：OBJECTIVE:Coronary artery disease has been described as one of the curses of the western world, as it is one of its most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary arter...

journal_title：Artificial intelligence in medicine

authors： Kukar M,Kononenko I,Grošelj C

更新日期：2011-06-01 00:00:00

abstract：OBJECTIVE:Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. INTRODUCTION:Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated med...

journal_title：Artificial intelligence in medicine

authors： Chapman WW,Christensen LM,Wagner MM,Haug PJ,Ivanov O,Dowling JN,Olszewski RT

更新日期：2005-01-01 00:00:00

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

authors： Kim S,Park T,Kon M

更新日期：2014-09-01 00:00:00

abstract：OBJECTIVE:Gene Ontology (GO) has become a routine resource for functional analysis of gene lists. Although a number of tools have been provided to identify enriched GO terms in one or two gene lists, two technical challenges remain. First, how to handle multiple hypothesis testing in the analysis given that the tests a...

journal_title：Artificial intelligence in medicine

authors： Zhong S,Xie D

更新日期：2007-10-01 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

authors： Bennett CC,Hauser K

更新日期：2013-01-01 00:00:00

abstract：OBJECTIVE:Suitable techniques for microarray analysis have been widely researched, particularly for the study of marker genes expressed to a specific type of cancer. Most of the machine learning methods that have been applied to significant gene selection focus on the classification ability rather than the selection ab...

journal_title：Artificial intelligence in medicine

authors： Tong DL,Schierz AC

更新日期：2011-09-01 00:00:00

abstract：:Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be ut...

journal_title：Artificial intelligence in medicine

authors： Seitz A,Uhrmacher AM,Damm D

更新日期：1999-03-01 00:00:00