当前位置： SCI文献检索 > ARTIFICIAL INTELLIGENCE IN MEDICINE期刊下所有文献 > Guideline-based careflow systems.

Guideline-based careflow systems.

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 management system'. It is based on computational formalisms representing both medical and health care organisational knowledge. This aggregation allows the implementation of a guideline, not only as a simple reminder, but also as an 'organiser' that facilitates health care processes. As a matter of fact, the system not only suggests the tasks to be performed, but also the resource allocation. The methodology initially comprehends a graphical editor, that allows an unambiguous representation of the guideline. Then the guideline is translated into a high-level Petri net. The resources, both human and technological necessary for performing guideline-based activities, are also represented by means of an organisational model. This allows the running of the Petri net for simulating the implementation of the guideline in the clinical setting. The purpose of the simulation is to validate the careflow model and to suggest the optimal resource allocation before the careflow system is installed. The final step is the careflow implementation. In this phase, we show that the 'workflow management' technology, widely used in business process automation, may be transferred to the health care setting. This requires augmenting the typical workflow management systems with the flexibility and the uncertainty management, typical of the health care processes. For illustrating the proposed methodology, we consider a guideline for the management of patients with acute ischemic stroke.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Quaglini S,Stefanelli M,Cavallini A,Micieli G,Fassino C,Mossa C

doi

10.1016/s0933-3657(00)00050-6

subject

Has Abstract

pub_date

2000-08-01 00:00:00

pages

5-22

issue

1

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(00)00050-6

journal_volume

20

pub_type

杂志文章

相关文献

ARTIFICIAL INTELLIGENCE IN MEDICINE文献大全
  • 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

  • Transferring brain-computer interfaces beyond the laboratory: successful application control for motor-disabled users.

    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

    authors： Leeb R,Perdikis S,Tonin L,Biasiucci A,Tavella M,Creatura M,Molina A,Al-Khodairy A,Carlson T,Millán JD

    更新日期：2013-10-01 00:00:00

  • Classifying free-text triage chief complaints into syndromic categories with natural language processing.

    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

    authors： Chapman WW,Christensen LM,Wagner MM,Haug PJ,Ivanov O,Dowling JN,Olszewski RT

    更新日期：2005-01-01 00:00:00

  • Identification of sympathetic and parasympathetic nerves function in cardiovascular regulation using ANFIS approximation.

    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 i...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.01.002

    authors： Jalali A,Ghaffari A,Ghorbanian P,Nataraj C

    更新日期：2011-05-01 00:00:00

  • From an expert-driven paper guideline to a user-centred decision support system: a usability comparison study.

    abstract：OBJECTIVE:To assess whether a user-centred prototype clinical decision support system (CDSS) providing patient-specific advice better supports healthcare practitioners in terms of (a) types of usability problems detected and (b) effective and efficient retrieval of childhood cancer survivor's follow-up screening proced...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2013.04.004

    authors： Kilsdonk E,Peute LW,Riezebos RJ,Kremer LC,Jaspers MW

    更新日期：2013-09-01 00:00:00

  • Instance-based classifiers applied to medical databases: diagnosis and knowledge extraction.

    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

    authors： Gagliardi F

    更新日期：2011-07-01 00:00:00

  • A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis.

    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

    authors： Gu D,Liang C,Zhao H

    更新日期：2017-03-01 00:00:00

  • Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods.

    abstract：:Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progr...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.101742

    authors： Zahia S,Garcia Zapirain MB,Sevillano X,González A,Kim PJ,Elmaghraby A

    更新日期：2020-01-01 00:00:00

  • Extracting rules from pruned networks for breast cancer diagnosis.

    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

    authors： Setiono R

    更新日期：1996-02-01 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

  • A knowledge-based clinical toxicology consultant for diagnosing multiple exposures.

    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

    authors： Schipper JD,Dankel DD 2nd,Arroyo AA,Schauben JL

    更新日期：2013-05-01 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

  • Modern parameterization and explanation techniques in diagnostic decision support system: a case study in diagnostics of coronary artery disease.

    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

    authors： Kukar M,Kononenko I,Grošelj C

    更新日期：2011-06-01 00:00:00

  • On the use of pairwise distance learning for brain signal classification with limited observations.

    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

    authors： Calhas D,Romero E,Henriques R

    更新日期：2020-05-01 00:00:00

  • Accessing complex patient data from Arden Syntax Medical Logic Modules.

    abstract：OBJECTIVE:Arden Syntax is a standard for representing and sharing medical knowledge in form of independent modules and looks back on a history of 25 years. Its traditional field of application is the monitoring of clinical events such as generating an alert in case of occurrence of a critical laboratory result. Arden S...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2015.09.003

    authors： Kraus S,Enders M,Prokosch HU,Castellanos I,Lenz R,Sedlmayr M

    更新日期：2018-11-01 00:00:00

  • Modelling prognostic power of cardiac tests using rough sets.

    abstract：:Rough sets (Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data, Dordrecht: Kluwer Academic Publishers, 1991) is a relatively new approach to representing and reasoning with incomplete and uncertain knowledge. This article introduces the basic concepts of rough sets and Boolean reasoning (Brown FM. Boole...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(98)00051-7

    authors： Komorowski J,Ohrn A

    更新日期：1999-02-01 00:00:00

  • The social phenotype: Extracting a patient-centered perspective of diabetes from health-related blogs.

    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

    authors： Lenzi A,Maranghi M,Stilo G,Velardi P

    更新日期：2019-11-01 00:00:00

  • Case-based reasoning for medical decision support tasks: the Inreca approach.

    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

    authors： Althoff KD,Bergmann R,Wess S,Manago M,Auriol E,Larichev OI,Bolotov A,Zhuravlev YI,Gurov SI

    更新日期：1998-01-01 00:00:00

  • User-defined functions in the Arden Syntax: An extension proposal.

    abstract：BACKGROUND:The Arden Syntax is a knowledge-encoding standard, started in 1989, and now in its 10th revision, maintained by the health level seven (HL7) organization. It has constructs borrowed from several language concepts that were available at that time (mainly the HELP hospital information system and the Regenstrie...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2015.11.003

    authors： Karadimas H,Ebrahiminia V,Lepage E

    更新日期：2018-11-01 00:00:00

  • 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

  • Bridge and brick network motifs: identifying significant building blocks from complex biological systems.

    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

    authors： Huang CY,Cheng CY,Sun CT

    更新日期：2007-10-01 00:00:00

  • Information extraction from multi-institutional radiology reports.

    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

    authors： Hassanpour S,Langlotz CP

    更新日期：2016-01-01 00:00:00

  • Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques.

    abstract：OBJECTIVE:This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does n...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.05.001

    authors： Monte-Moreno E

    更新日期：2011-10-01 00:00:00

  • Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

    abstract：OBJECTIVE:This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation i...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2013.12.006

    authors： Jiménez F,Sánchez G,Juárez JM

    更新日期：2014-03-01 00:00:00

  • Development of systems for support of collaboration in health care: the design arenas.

    abstract：:To explore the design of computer-supported collaborative work in health care, a case study is described addressing the social contexts and conditions influencing the development process. The data set covers 13 consecutive meetings held in a systems design group over a 2-year period, in total approximately 24 h of vid...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(97)00046-8

    authors： Timpka T,Sjöberg C

    更新日期：1998-02-01 00:00:00

  • Inconsistency as a diagnostic tool in a society of intelligent agents.

    abstract：OBJECTIVE:To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. METHODS:We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent ag...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2012.04.005

    authors： McShane M,Beale S,Nirenburg S,Jarrell B,Fantry G

    更新日期：2012-07-01 00:00:00

  • Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text.

    abstract：:Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting kn...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.01.015

    authors： Cao C,Wang H,Sui Y

    更新日期：2004-09-01 00:00:00

  • Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

    abstract：:Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101911

    authors： Lee WK,Wu CC,Lee CC,Lu CF,Yang HC,Huang TH,Lin CY,Chung WY,Wang PS,Wu HM,Guo WY,Wu YT

    更新日期：2020-07-01 00:00:00

  • Granular support vector machines with association rules mining for protein homology prediction.

    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

    authors： Tang Y,Jin B,Zhang YQ

    更新日期：2005-09-01 00:00:00