当前位置： SCI文献检索 > ARTIFICIAL INTELLIGENCE IN MEDICINE期刊下所有文献 > An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem.

An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem.

Abstract：

OBJECTIVE:The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting patient comfort and treatment efficiency are maximized. Consideration must be given to uncertainty in the length of stays of patients as well as the possibility of emergency patients. METHOD:We develop an adaptive large neighborhood search (ALNS) procedure to solve the problem. This procedure utilizes a Simulated Annealing framework. RESULTS:We thoroughly test the performance of the proposed ALNS approach on a set of 450 publicly available problem instances. A comparison with the current state-of-the-art indicates that the proposed methodology provides solutions that are of comparable quality for small and medium sized instances (up to 1000 patients); the two approaches provide solutions that differ in quality by approximately 1% on average. The ALNS procedure does, however, provide solutions in a much shorter time frame. On larger instances (between 1000-4000 patients) the improvement in solution quality by the ALNS procedure is substantial, approximately 3-14% on average, and as much as 22% on a single instance. The time taken to find such results is, however, in the worst case, a factor 12 longer on average than the time limit which is granted to the current state-of-the-art. CONCLUSION:The proposed ALNS procedure is an efficient and flexible method for solving the DPAS problem.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Lusby RM,Schwierz M,Range TM,Larsen J

doi

10.1016/j.artmed.2016.10.002

subject

Has Abstract

pub_date

2016-11-01 00:00:00

pages

21-31

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(16)30092-6

journal_volume

74

pub_type

杂志文章

相关文献

ARTIFICIAL INTELLIGENCE IN MEDICINE文献大全
  • Component-based mediation services for the integration of medical applications.

    abstract：:Allowing exchange of information and cooperation among network-wide distributed and heterogeneous applications is a major need of current health-care information systems. The European project SynEx aims at developing an integration platform for both new and legacy applications on each partner's site. We developed, in ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(03)00007-1

    authors： Xu Y,Sauquet D,Degoulet P,Jaulent MC

    更新日期：2003-03-01 00:00:00

  • Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis.

    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

    authors： Xie J,Jiang J,Wang Y,Guan Y,Guo X

    更新日期：2020-07-01 00:00:00

  • ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network.

    abstract：:Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method fo...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101856

    authors： Zhang J,Liu A,Gao M,Chen X,Zhang X,Chen X

    更新日期：2020-06-01 00:00:00

  • State recognition of decompressive laminectomy with multiple information in robot-assisted surgery.

    abstract：:The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons h...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.101763

    authors： Sun Y,Wang L,Jiang Z,Li B,Hu Y,Tian W

    更新日期：2020-01-01 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

  • Cancer survival classification using integrated data sets and intermediate information.

    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

    authors： Kim S,Park T,Kon M

    更新日期：2014-09-01 00:00:00

  • Out of hours workload management: Bayesian inference for decision support in secondary care.

    abstract：OBJECTIVE:In this paper, we aim to evaluate the use of electronic technologies in out of hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2016.09.005

    authors： Perez I,Brown M,Pinchin J,Martindale S,Sharples S,Shaw D,Blakey J

    更新日期：2016-10-01 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

  • 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

  • Neural network based classification of single-trial EEG data.

    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

    authors： Masic N,Pfurtscheller G

    更新日期：1993-12-01 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • Patterns for collaborative work in health care teams.

    abstract：OBJECTIVE:The problem of designing and managing teams of workers that can collaborate working together towards common goals is a challenging one. Incomplete or ambiguous specification of responsibilities and accountabilities, lack of continuity in teams working in shifts, inefficient organization of teams due to lack o...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.08.005

    authors： Grando MA,Peleg M,Cuggia M,Glasspool D

    更新日期：2011-11-01 00:00:00

  • Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

    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

    authors： Tung C,Lu W

    更新日期：2016-01-01 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

  • An object-oriented approach to knowledge representation in a biomedical domain.

    abstract：:An object-oriented approach has been applied to the different stages involved in developing a knowledge base about insulin metabolism. At an early stage the separation of terminological and assertional knowledge was made. The terminological component was developed by medical experts and represented in CORE. An object-...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/0933-3657(94)90025-6

    authors： Ensing M,Paton R,Speel PH,Rada R

    更新日期：1994-12-01 00:00:00

  • A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis.

    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

    authors： Wang J,Hu Y,Xiao F,Deng X,Deng Y

    更新日期：2016-05-01 00:00:00

  • Bounded-depth threshold circuits for computer-assisted CT image classification.

    abstract：:We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed,...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(01)00101-4

    authors： Albrecht A,Hein E,Steinhöfel K,Taupitz M,Wong CK

    更新日期：2002-02-01 00:00:00

  • Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies.

    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

    authors： Lamy JB

    更新日期：2017-07-01 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

  • 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

  • Renoir, Pneumon-IA and Terap-IA: three medical applications based on fuzzy logic.

    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

    authors： Godo L,de Mántaras RL,Puyol-Gruart J,Sierra C

    更新日期：2001-01-01 00:00:00

  • Bayesian network multi-classifiers for protein secondary structure prediction.

    abstract：:Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.01.009

    authors： Robles V,Larrañaga P,Peña JM,Menasalvas E,Pérez MS,Herves V,Wasilewska A

    更新日期：2004-06-01 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

  • Modeling and solving the dynamic patient admission scheduling problem under uncertainty.

    abstract：OBJECTIVE:Our goal is to propose and solve a new formulation of the recently-formalized patient admission scheduling problem, extending it by including several real-world features, such as the presence of emergency patients, uncertainty in the length of stay, and the possibility of delayed admissions. METHOD:We devise...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2012.09.001

    authors： Ceschia S,Schaerf A

    更新日期：2012-11-01 00:00:00

  • Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

    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

    authors： Erdogdu U,Polat F,Alhajj R

    更新日期：2017-11-01 00:00:00