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 relationships between existing models of rationality and bounded rationality for similar tasks. Models of rationality refer to those approaches in which the optimal properties of the models are deductively provable, i.e. in which the processing is rational. Representative models of rationality used in AIM are deductive logical models, statistical models such as Bayesian inference models, and decision-analytic models. Models of bounded rationality are those which do not guarantee such optimal properties nor yield to deductive correctness proofs. These models have their roots in cognitive psychology. In this article we show how explicating the relationship between models of rationality and bounded rationality might be done in the case of abductive tasks in medicine. This is done by positioning these modeling approaches within the same framework (an abstract computational model) and interpreting in this context both computational complexity results concerning the nature of the task and empirical results studies of human problem-solving behavior.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Smith JW Jr,Bayazitoglu Adoi
10.1016/0933-3657(93)90013-ssubject
Has Abstractpub_date
1993-04-01 00:00:00pages
125-42issue
2eissn
0933-3657issn
1873-2860journal_volume
5pub_type
杂志文章,评审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
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00055-6
更新日期：2002-09-01 00:00:00
abstract：OBJECTIVE:The objective of this paper is to classify 3D medical images by analyzing spatial distributions to model and characterize the arrangement of the regions of interest (ROIs) in 3D space. METHODS AND MATERIAL:Two methods are proposed for facilitating such classification. The first method uses measures of simila...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.07.001
更新日期：2005-03-01 00:00:00
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
更新日期：2016-02-01 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：:This paper describes a methodology for achieving an efficient implementation of clinical practice guidelines. Three main steps are illustrated: knowledge representation, model simulation and implementation within a health care organisation. The resulting system can be classified as a 'guideline-based careflow manageme...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(00)00050-6
更新日期：2000-08-01 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
pub_type： 杂志文章
doi：10.1016/j.artmed.2018.08.004
更新日期：2019-04-01 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
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.101783
更新日期：2020-03-01 00:00:00
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
更新日期：2017-06-01 00:00:00
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
更新日期：2020-06-01 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：OBJECTIVE:The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining technology to analyze and predict the depression tendency of web posts. ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2015.10.003
更新日期：2016-01-01 00:00:00
abstract：OBJECTIVE:Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The ma...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.014
更新日期：2009-02-01 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
pub_type： 杂志文章
doi：10.1016/s0933-3657(97)00038-9
更新日期：1998-01-01 00:00:00
abstract：OBJECTIVE:This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY:The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2005.09.002
更新日期：2006-05-01 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：:Medical diagnosis and therapy planning at modern intensive care units (ICUs) have been refined by the technical improvement of their equipment. However, the bulk of continuous data arising from complex monitoring systems in combination with discontinuously assessed numerical and qualitative data creates a rising infor...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(96)00355-7
更新日期：1996-11-01 00:00:00
abstract：OBJECTIVE:We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their m...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2017.02.003
更新日期：2017-03-01 00:00:00
abstract：:The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as features of these structures are directly related to the diagnosis and trea...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.07.010
更新日期：2019-08-01 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
pub_type： 杂志文章
doi：10.1016/j.artmed.2006.07.010
更新日期：2007-02-01 00:00:00
abstract：:The research at the IIIA has produced over more than a decade two versions of a tool for developing knowledge-based systems: Milord and Milord II. This tool has been mainly used for the development of medical applications. In this paper we summarize the Milord II approximate reasoning approach based on fuzzy sets, and...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(00)00080-4
更新日期：2001-01-01 00:00:00
abstract：OBJECTIVE:Is it possible to predict the severity staging of a Parkinson's disease (PD) patient using scores of non-motor symptoms? This is the kickoff question for a machine learning approach to classify two widely known PD severity indexes using individual tests from a broad set of non-motor PD clinical scales only. ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.04.002
更新日期：2013-07-01 00:00:00
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
更新日期：2003-03-01 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：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 trea...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2012.03.002
更新日期：2012-07-01 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
abstract：OBJECTIVE:The complex cognitive processes that underlie human performance in 'messy' contexts such as critical care medicine suggest a need for a cognitive model with broad scope to support the understanding of error in such domains. The objective of this research is to characterize the cognition that underlies patient...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2006.03.009
更新日期：2006-06-01 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: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
更新日期：2005-07-01 00:00:00
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: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
