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 a frequently revised service, subject to increasing pressures. METHODS AND MATERIAL:We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. RESULTS:Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. CONCLUSIONS:The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

journal_name

Artif Intell Med

authors

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

doi

10.1016/j.artmed.2016.09.005

subject

Has Abstract

pub_date

2016-10-01 00:00:00

pages

34-44

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(16)30155-5

journal_volume

73

pub_type

杂志文章
  • Automatic processing of multilingual medical terminology: applications to thesaurus enrichment and cross-language information retrieval.

    abstract:OBJECTIVES:We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains. MATERIAL AND METHODS:We propose firstly a method for enriching mu...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2004.07.015

    authors: Déjean H,Gaussier E,Renders JM,Sadat F

    更新日期:2005-02-01 00:00:00

  • A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy.

    abstract:BACKGROUND:The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspec...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101751

    authors: Campos M,Jimenez F,Sanchez G,Juarez JM,Morales A,Canovas-Segura B,Palacios F

    更新日期:2020-01-01 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

  • 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

  • Case-based prediction in experimental medical studies.

    abstract::Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be ut...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(98)00057-8

    authors: Seitz A,Uhrmacher AM,Damm D

    更新日期:1999-03-01 00:00:00

  • 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...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2016.10.002

    authors: Lusby RM,Schwierz M,Range TM,Larsen J

    更新日期:2016-11-01 00:00:00

  • Identifying the measurement noise in glaucomatous testing: an artificial neural network approach.

    abstract::Diagnosis of visual function losses in glaucomatous patients depends to a large extent on the analysis of the data collected from corresponding psychophysical tests. One of the main difficulties in obtaining reliable data from patients in these tests is the measurement noise caused by the learning effect, inattention,...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/0933-3657(94)90004-3

    authors: Liu X,Cheng G,Wu JX

    更新日期:1994-10-01 00:00:00

  • Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection.

    abstract::Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning model, such as faster region convolutional neural network (faster R-CNN), has be...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101881

    authors: Chen S,Han Y,Lin J,Zhao X,Kong P

    更新日期:2020-07-01 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

  • Automatic computation of mandibular indices in dental panoramic radiographs for early osteoporosis detection.

    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

    authors: Aliaga I,Vera V,Vera M,García E,Pedrera M,Pajares G

    更新日期:2020-03-01 00:00:00

  • Detecting signals of detrimental prescribing cascades from social media.

    abstract:MOTIVATION:Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2016.06.002

    authors: Hoang T,Liu J,Pratt N,Zheng VW,Chang KC,Roughead E,Li J

    更新日期:2016-07-01 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

  • 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

  • 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

  • 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

  • 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

  • A multiple classifier system for early melanoma diagnosis.

    abstract::Melanoma is the most dangerous skin cancer and early diagnosis is the key factor in its successful treatment. Well-trained dermatologists reach a diagnosis via visual inspection, and reach sensitivity and specificity levels of about 80%. Several computerised diagnostic systems were reported in the literature using dif...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(02)00087-8

    authors: Sboner A,Eccher C,Blanzieri E,Bauer P,Cristofolini M,Zumiani G,Forti S

    更新日期:2003-01-01 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

  • Brain-controlled applications using dynamic P300 speller matrices.

    abstract:OBJECTIVES:Access to the world wide web and multimedia content is an important aspect of life. We present a web browser and a multimedia user interface adapted for control with a brain-computer interface (BCI) which can be used by severely motor impaired persons. METHODS:The web browser dynamically determines the most...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2014.12.001

    authors: Halder S,Pinegger A,Käthner I,Wriessnegger SC,Faller J,Pires Antunes JB,Müller-Putz GR,Kübler A

    更新日期:2015-01-01 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

  • 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

  • A cognitive blueprint of collaboration in context: distributed cognition in the psychiatric emergency department.

    abstract:OBJECTIVE:The complex cognitive processes that underlie human performance in 'messy' contexts such as critical care medicine suggest a need for a cognitive model with broad scope to support the understanding of error in such domains. The objective of this research is to characterize the cognition that underlies patient...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2006.03.009

    authors: Cohen T,Blatter B,Almeida C,Shortliffe E,Patel V

    更新日期:2006-06-01 00:00:00

  • 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 ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(98)00027-x

    authors: Pesonen E,Eskelinen M,Juhola M

    更新日期:1998-07-01 00:00:00

  • Temporal similarity measures for querying clinical workflows.

    abstract:OBJECTIVE:In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal concept...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2008.07.013

    authors: Combi C,Gozzi M,Oliboni B,Juarez JM,Marin R

    更新日期:2009-05-01 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

  • Fuzzy ensemble clustering based on random projections for DNA microarray data analysis.

    abstract:OBJECTIVE:Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The ma...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2008.07.014

    authors: Avogadri R,Valentini G

    更新日期:2009-02-01 00:00:00

  • Using Arden Syntax for the creation of a multi-patient surveillance dashboard.

    abstract:OBJECTIVE:Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not native...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2015.09.009

    authors: Kraus S,Drescher C,Sedlmayr M,Castellanos I,Prokosch HU,Toddenroth D

    更新日期:2018-11-01 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

  • The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

    abstract::Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer p...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2019.101706

    authors: Spänig S,Emberger-Klein A,Sowa JP,Canbay A,Menrad K,Heider D

    更新日期:2019-09-01 00:00:00

  • Continual planning and scheduling for managing patient tests in hospital laboratories.

    abstract::Hospital laboratories perform examination tests upon patients, in order to assist medical diagnosis or therapy progress. Planning and scheduling patient requests for examination tests is a complicated problem because it concerns both minimization of patient stay in hospital and maximization of laboratory resources uti...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(00)00061-0

    authors: Marinagi CC,Spyropoulos CD,Papatheodorou C,Kokkotos S

    更新日期:2000-10-01 00:00:00