Treatment of missing data values in a neural network based decision support system for acute abdominal pain.

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 Med

authors

Pesonen E,Eskelinen M,Juhola M

doi

10.1016/s0933-3657(98)00027-x

subject

Has Abstract

pub_date

1998-07-01 00:00:00

pages

139-46

issue

3

eissn

0933-3657

issn

1873-2860

pii

S093336579800027X

journal_volume

13

pub_type

杂志文章
  • Topic-informed neural approach for biomedical event extraction.

    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

    authors: Zhang J,Liu M,Zhang Y

    更新日期:2020-03-01 00:00:00

  • Adaptive dynamic programming algorithms for sequential appointment scheduling with patient preferences.

    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

    authors: Wang J,Fung RY

    更新日期:2015-01-01 00:00:00

  • A novel deep mining model for effective knowledge discovery from omics data.

    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

    authors: Alzubaidi A,Tepper J,Lotfi A

    更新日期:2020-04-01 00:00:00

  • A hybrid expert system for the diagnosis of epileptic crisis.

    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

    authors: Brasil LM,de Azevedo FM,Barreto JM

    更新日期:2001-01-01 00:00:00

  • Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns.

    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

    authors: Ré M,Pavesi G

    更新日期:2009-02-01 00:00:00

  • Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions.

    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

    authors: Paetz J

    更新日期:2003-06-01 00:00:00

  • Towards a computer-aided diagnosis system for vocal cord diseases.

    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

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

    更新日期:2006-01-01 00:00:00

  • Generating recipient-centered explanations about drug prescription.

    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

    authors: De Carolis B,de Rosis F,Grasso F,Rossiello A,Berry DC,Gillie T

    更新日期:1996-05-01 00:00:00

  • Medical dictionaries for patient encoding systems: a methodology.

    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

    authors: Lovis C,Baud R,Rassinoux AM,Michel PA,Scherrer JR

    更新日期:1998-09-01 00:00:00

  • Selection of patients for clinical trials: an interactive web-based system.

    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

    authors: Fink E,Kokku PK,Nikiforou S,Hall LO,Goldgof DB,Krischer JP

    更新日期:2004-07-01 00:00:00

  • Comparative study of approximate entropy and sample entropy robustness to spikes.

    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

    authors: Molina-Picó A,Cuesta-Frau D,Aboy M,Crespo C,Miró-Martínez P,Oltra-Crespo S

    更新日期:2011-10-01 00:00:00

  • Constructing explanatory process models from biological data and knowledge.

    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

    authors: Langley P,Shiran O,Shrager J,Todorovski L,Pohorille A

    更新日期:2006-07-01 00:00:00

  • Terminological resources for text mining over biomedical scientific literature.

    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

    authors: Rinaldi F,Kaljurand K,Sætre R

    更新日期:2011-06-01 00:00:00

  • Automatic classification of epilepsy types using ontology-based and genetics-based machine learning.

    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

    authors: Kassahun Y,Perrone R,De Momi E,Berghöfer E,Tassi L,Canevini MP,Spreafico R,Ferrigno G,Kirchner F

    更新日期:2014-06-01 00:00:00

  • Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks.

    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

    authors: Aussem A,de Morais SR,Corbex M

    更新日期:2012-01-01 00:00:00

  • Using cognitive task analysis to facilitate the integration of decision support systems into the neonatal intensive care unit.

    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

    authors: Baxter GD,Monk AF,Tan K,Dear PR,Newell SJ

    更新日期:2005-11-01 00:00:00

  • A fuzzy cognitive map approach to differential diagnosis of specific language impairment.

    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

    authors: Georgopoulos VC,Malandraki GA,Stylios CD

    更新日期:2003-11-01 00:00:00

  • Anatomical sketch understanding: recognizing explicit and implicit structure.

    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

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

    更新日期:2007-02-01 00:00:00

  • Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model.

    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

    authors: Becker K,Thull B,Käsmacher-Leidinger H,Stemmer J,Rau G,Kalff G,Zimmermann HJ

    更新日期:1997-09-01 00:00:00

  • Specification of models in large expert systems based on causal probabilistic networks.

    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

    authors: Olesen KG,Andreassen S

    更新日期:1993-06-01 00:00:00

  • Finding temporal patterns--a set-based approach.

    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

    authors: Wade TD,Byrns PJ,Steiner JF,Bondy J

    更新日期:1994-06-01 00:00:00

  • Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors.

    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

    authors: Okun O,Priisalu H

    更新日期:2009-02-01 00:00:00

  • A frame reduction system based on a color structural similarity (CSS) method and Bayer images analysis for capsule endoscopy.

    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

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

    更新日期:2019-03-01 00:00:00

  • Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents.

    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

    authors: Esmaili N,Buchlak QD,Piccardi M,Kruger B,Girosi F

    更新日期:2021-01-01 00:00:00

  • Applying spatial distribution analysis techniques to classification of 3D medical images.

    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

    authors: Pokrajac D,Megalooikonomou V,Lazarevic A,Kontos D,Obradovic Z

    更新日期:2005-03-01 00:00:00

  • Project INSIDE: towards autonomous semi-unstructured human-robot social interaction in autism therapy.

    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

    authors: Melo FS,Sardinha A,Belo D,Couto M,Faria M,Farias A,Gambôa H,Jesus C,Kinarullathil M,Lima P,Luz L,Mateus A,Melo I,Moreno P,Osório D,Paiva A,Pimentel J,Rodrigues J,Sequeira P,Solera-Ureña R,Vasco M,Veloso M,Vent

    更新日期:2019-05-01 00:00:00

  • Computer models of hippocampal circuit changes of the kindling model of epilepsy.

    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

    authors: Lytton WW,Hellman KM,Sutula TP

    更新日期:1998-05-01 00:00:00

  • Identification of the optic nerve head with genetic algorithms.

    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

    authors: Carmona EJ,Rincón M,García-Feijoó J,Martínez-de-la-Casa JM

    更新日期:2008-07-01 00:00:00

  • Exploring the relationship between rationality and bounded rationality in medical knowledge-based systems.

    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

    authors: Smith JW Jr,Bayazitoglu A

    更新日期:1993-04-01 00:00:00

  • iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

    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

    authors: Akbar S,Hayat M,Iqbal M,Jan MA

    更新日期:2017-06-01 00:00:00