Abstract:
OBJECTIVE:In this paper a new nonlinear system identification approach is developed for dynamical quantification of cardiovascular regulation. This approach is specifically focused on the identification of the heart rate (HR) baroreflex mechanism. The principal objective of this paper is to improve the model accuracy in the estimation of HR by proposing a modified nonlinear model. METHODS AND MATERIAL:The proposed HR baroreflex model is based on inherent features of the autonomic nervous system for which we develop an adaptive neuro-fuzzy inference system (ANFIS) structure. This method allows incorporation of physiological understandings about the sympathetic and parasympathetic nerves through the selection of appropriate membership functions in the ANFIS structure. The required data for system modeling are collected from the publicly available PhysioNet database. RESULTS:The results agree with the natural characteristics and physiological understanding of the cardiovascular regulatory system, such as delay in the parasympathetic function, durability in the function of sympathetic nerves and the correlation between the HR and the ABP signals. They also show significant improvements in HR prediction in terms of the normalized root mean square error (NRMSE) in comparison with other reported methods. We achieved to 0.191 in mean NRMSE in prediction of HR in this paper which is about 20% better than the best reported result in other researches. CONCLUSION:We have shown that for cardiovascular system regulation, our proposed nonlinear model is more accurate than other recently developed methods. Accurate HR baroreflex modeling enables clinicians to have more reliable information for their patients.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Jalali A,Ghaffari A,Ghorbanian P,Nataraj Cdoi
10.1016/j.artmed.2011.01.002subject
Has Abstractpub_date
2011-05-01 00:00:00pages
27-32issue
1eissn
0933-3657issn
1873-2860pii
S0933-3657(11)00003-0journal_volume
52pub_type
杂志文章abstract:BACKGROUND:After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more inform...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2016.01.001
更新日期:2016-02-01 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::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::The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral pr...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101852
更新日期:2020-05-01 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: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::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::According to functional or anatomical modalities, medical imaging provides a visual representation of complex structures or activities in the human body. One of the most common processing methods applied to those images is segmentation, in which an image is divided into a set of regions of interest. Human anatomical c...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101980
更新日期:2020-11-01 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
pub_type: 杂志文章
doi:10.1016/j.artmed.2008.07.003
更新日期:2008-10-01 00:00:00
abstract:OBJECTIVE:Recently, fuzzy soft sets-based decision making has attracted more and more interest. Although plenty of works have been done, they cannot provide the uncertainty or certainty of their results. To manage uncertainty is one of the most important and toughest tasks of decision making especially in medicine. In ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2016.04.004
更新日期:2016-05-01 00:00:00
abstract::INTERNIST-I was an expert system designed in the early 1970's to diagnose multiple diseases in internal medicine by modelling the behaviour of clinicians. Its form and operation are described, and evaluations of the system are surveyed. The major result of the project was its knowledge base which has been used in succ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/0933-3657(94)00028-q
更新日期:1995-04-01 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: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: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::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:OBJECTIVE:Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an ess...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2005.02.003
更新日期:2005-09-01 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: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
pub_type: 杂志文章
doi:10.1016/j.artmed.2014.06.003
更新日期:2014-09-01 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
pub_type: 杂志文章
doi:10.1016/0933-3657(93)90040-a
更新日期:1993-12-01 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::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:OBJECTIVE:The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing int...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2006.08.005
更新日期:2007-02-01 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::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 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
pub_type: 杂志文章
doi:10.1016/j.artmed.2007.04.003
更新日期:2007-07-01 00:00:00
abstract::Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101950
更新日期:2020-08-01 00:00:00
abstract:OBJECTIVE:Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2017.07.002
更新日期:2017-07-01 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::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::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
pub_type: 杂志文章
doi:10.1016/s0933-3657(02)00005-2
更新日期:2002-05-01 00:00:00