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 images were divided into segments based on the Yale system. Each segment was described by the physician as showing one of the following conditions: normal perfusion, reversible perfusion defect, partially reversible perfusion defect, fixed perfusion defect, defect showing reverse redistribution, equivocal defect or artifact. The physician's diagnosis of overall left ventricular (LV) perfusion, based on the above descriptions, categorizes a study as showing one or more of eight possible conditions: normal, ischemia, infarct and ischemia, infarct, reverse redistribution, equivocal, artifact or LV dysfunction. Because of the complexity of the task, we decided to use the knowledge discovery approach, consisting of these steps: problem understanding, data understanding, data preparation, data mining, evaluating the discovered knowledge and its implementation. After going through the data preparation step, in which we constructed normal gender-specific models of the LV and image registration, we ended up with 728 patients for whom we had both SPECT images and corresponding diagnoses. Another major contribution of the paper is the data mining step, in which we used several new Bayesian learning classification methods. The approach we have taken, namely the six-step knowledge discovery process has proven to be very successful in this complex data mining task and as such the process can be extended to other medical data mining projects.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Sacha JP,Goodenday LS,Cios KJdoi
10.1016/s0933-3657(02)00055-6subject
Has Abstractpub_date
2002-09-01 00:00:00pages
109-143issue
1-2eissn
0933-3657issn
1873-2860pii
S0933365702000556journal_volume
26pub_type
杂志文章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
更新日期：1997-01-01 00:00:00
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
更新日期：2002-07-01 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：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
更新日期：2016-01-01 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:Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs). METHODS AND MATERIAL:Different approaches exist to model GRNs; they are mostly simulated as mathematical models that represent relationships between ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2017.06.007
更新日期：2017-11-01 00:00:00
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
更新日期：1993-06-01 00:00:00
abstract：:Melanoma is the most dangerous skin cancer and early diagnosis is the key factor in its successful treatment. Well-trained dermatologists reach a diagnosis via visual inspection, and reach sensitivity and specificity levels of about 80%. Several computerised diagnostic systems were reported in the literature using dif...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00087-8
更新日期：2003-01-01 00:00:00
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
更新日期：2005-02-01 00:00:00
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
更新日期：2007-10-01 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
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.04.009
更新日期：2011-06-01 00:00:00
abstract：OBJECTIVE:New medical systems may be rejected by staff because they do not integrate with local practice. An expert system, FLORENCE, is being developed to help staff in a neonatal intensive care unit (NICU) make decisions about ventilator settings when treating babies with respiratory distress syndrome. For FLORENCE t...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2005.01.004
更新日期：2005-11-01 00:00:00
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
更新日期：2013-10-01 00:00:00
abstract：OBJECTIVE:The aim of this paper is to study the feasibility and the performance of some classifier systems belonging to family of instance-based (IB) learning as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in clinical databases. MATERIALS AND ME...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.04.002
更新日期：2011-07-01 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
pub_type： 杂志文章,评审
doi：10.1016/s0933-3657(01)00087-2
更新日期：2001-11-01 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
pub_type： 杂志文章
doi：10.1016/j.artmed.2017.03.012
更新日期：2017-09-01 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
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.04.001
更新日期：2005-01-01 00:00:00
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
更新日期：2011-06-01 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:The successful preparation of cells for therapy depends on the characterization of causal factors affecting cell quality. Ultra scale-down methods are used to characterize cells in terms of their response to process engineering causal factors of hydrodynamic shear stress and time. This response is in turn cha...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.07.003
更新日期：2014-10-01 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：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
更新日期：2016-07-01 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
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00040-2
更新日期：1998-11-01 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
pub_type： 杂志文章
doi：10.1016/0933-3657(95)00019-4
更新日期：1996-02-01 00:00:00
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
更新日期：2004-07-01 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：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：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
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.11.001
更新日期：2006-01-01 00:00:00
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
更新日期：2009-10-01 00:00:00
abstract：:This paper describes the INSIDE system, a networked robot system designed to allow the use of mobile robots as active players in the therapy of children with autism spectrum disorders (ASD). While a significant volume of work has explored the impact of robots in ASD therapy, most such work comprises remotely operated ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2018.12.003
更新日期：2019-05-01 00:00:00
