Abstract:
:In this study different substitution methods for the replacement of missing data values were inspected for the use of these cases in a neural network based decision support system for acute appendicitis. The leucocyte count had the greatest number of missing values and was used in the analyses. Four different methods were compared: substituting means, random values, nearest neighbour and a neural network. There were great differences in the substituted leucocyte count values between different methods and only nearest neighbour and neural network agreed about most of the cases. The importance of the substitution method for the final diagnostic classification of the patients by the neural network based decision support system was found to be small.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Pesonen E,Eskelinen M,Juhola Mdoi
10.1016/s0933-3657(98)00027-xsubject
Has Abstractpub_date
1998-07-01 00:00:00pages
139-46issue
3eissn
0933-3657issn
1873-2860pii
S093336579800027Xjournal_volume
13pub_type
杂志文章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:OBJECTIVES:A well-developed appointment system can help increase the utilization of medical facilities in an outpatient department. This paper outlines the development of an appointment system that can make an outpatient department work more efficiently and improve patient satisfaction level. METHODS:A Markov decision...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2014.12.002
更新日期:2015-01-01 00:00:00
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
更新日期:2020-04-01 00:00:00
abstract::This work presents a hybrid expert system (HES) intended to minimise some complex problems pervasive to knowledge engineering such as: the knowledge elicitation process, known as the bottleneck of expert systems; the choice of a model for knowledge representation to codify human reasoning; the number of neurons in the...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(00)00090-7
更新日期:2001-01-01 00:00:00
abstract:OBJECTIVE:In the last few years several complete genome sequences have been made available to the research community. The annotation of their complete inventory of protein coding genes, however, has been so far an elusive goal. Classical ab initio gene prediction methods have been of great support for this task, but sh...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2008.07.015
更新日期:2009-02-01 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: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::In this paper we describe how we generated written explanations to 'indirect users' of a knowledge-based system in the domain of drug prescription. We call 'indirect users' the intended recipients of explanations, to distinguish them from the prescriber (the 'direct' user) who interacts with the system. The Explanatio...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/0933-3657(95)00029-1
更新日期:1996-05-01 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::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: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
更新日期:2011-10-01 00:00:00
abstract:OBJECTIVE:We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. METHODS:We cast both models and background knowledge in terms of processes that interact to account for behavior. We a...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2006.04.003
更新日期:2006-07-01 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:OBJECTIVES:In the presurgical analysis for drug-resistant focal epilepsies, the definition of the epileptogenic zone, which is the cortical area where ictal discharges originate, is usually carried out by using clinical, electrophysiological and neuroimaging data analysis. Clinical evaluation is based on the visual det...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2014.03.001
更新日期:2014-06-01 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: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::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: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 process of patient care performed by an anaesthesiologist during high invasive surgery requires fundamental knowledge of the physiologic processes and a long standing experience in patient management to cope with the inter-individual variability of the patients. Biomedical engineering research improves the patient...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(97)00020-1
更新日期:1997-09-01 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::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
更新日期:1994-06-01 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::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
pub_type: 杂志文章
doi:10.1016/j.artmed.2018.12.008
更新日期:2019-03-01 00:00:00
abstract:BACKGROUND:Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101997
更新日期:2021-01-01 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::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
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: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::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
pub_type: 杂志文章,评审
doi:10.1016/0933-3657(93)90013-s
更新日期:1993-04-01 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