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  • 1. Beyene, Ayne A.
    et al.
    Welemariam, Tewelle
    Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Improved concept drift handling in surgery prediction and other applications2015In: Knowledge and Information Systems, ISSN 0219-1377, Vol. 44, no 1, p. 177-196Article in journal (Refereed)
    Abstract [en]

    The article presents a new algorithm for handling concept drift: the Trigger-based Ensemble (TBE) is designed to handle concept drift in surgery prediction but it is shown to perform well for other classification problems as well. At the primary care, queries about the need for surgical treatment are referred to a surgeon specialist. At the secondary care, referrals are reviewed by a team of specialists. The possible outcomes of this review are that the referral: (i) is canceled, (ii) needs to be complemented, or (iii) is predicted to lead to surgery. In the third case, the referred patient is scheduled for an appointment with a surgeon specialist. This article focuses on the binary prediction of case three (surgery prediction). The guidelines for the referral and the review of the referral are changed due to, e.g., scientific developments and clinical practices. Existing decision support is based on the expert systems approach, which usually requires manual updates when changes in clinical practice occur. In order to automatically revise decision rules, the occurrence of concept drift (CD) must be detected and handled. The existing CD handling techniques are often specialized; it is challenging to develop a more generic technique that performs well regardless of CD type. Experiments are conducted to measure the impact of CD on prediction performance and to reduce CD impact. The experiments evaluate and compare TBE to three existing CD handling methods (AWE, Active Classifier, and Learn++) on one real-world dataset and one artificial dataset. TBA significantly outperforms the other algorithms on both datasets but is less accurate on noisy synthetic variations of the real-world dataset.

  • 2.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Andersson, Petter
    Engineering Method Development, GKN Aerospace Engine Systems Sweden.
    Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tree-Based Response Surface Analysis2015Conference paper (Refereed)
    Abstract [en]

    Computer-simulated experiments have become a cost effective way for engineers to replace real experiments in the area of product development. However, one single computer-simulated experiment can still take a significant amount of time. Hence, in order to minimize the amount of simulations needed to investigate a certain design space, different approaches within the design of experiments area are used. One of the used approaches is to minimize the time consumption and simulations for design space exploration through response surface modeling. The traditional methods used for this purpose are linear regression, quadratic curve fitting and support vector machines. This paper analyses and compares the performance of four machine learning methods for the regression problem of response surface modeling. The four methods are linear regression, support vector machines, M5P and random forests. Experiments are conducted to compare the performance of tree models (M5P and random forests) with the performance of non-tree models (support vector machines and linear regression) on data that is typical for concept evaluation within the aerospace industry. The main finding is that comprehensible models (the tree models) perform at least as well as or better than traditional black-box models (the non-tree models). The first observation of this study is that engineers understand the functional behavior, and the relationship between inputs and outputs, for the concept selection tasks by using comprehensible models. The second observation is that engineers can also increase their knowledge about design concepts, and they can reduce the time for planning and conducting future experiments.

  • 3. Davidsson, Paul
    et al.
    Holmgren, Johan
    Kyhlbäck, Hans
    Mengistu, Dawit
    Persson, Marie
    Applications of Agent Based Simulation2007Conference paper (Refereed)
    Abstract [en]

    This paper provides a survey and analysis of applications of Agent Based Simulation (ABS). A framework for describing and assessing the applications is presented and systematically applied. A general conclusion from the study is that even if ABS seems a promising approach to many problems involving simulation of complex systems of interacting entities, it seems as the full potential of the agent concept and previous research and development within ABS often is not utilized. We illustrate this by providing some concrete examples. Another conclusion is that important information of the applications, in particular concerning the implementation of the simulator, was missing in many papers. As an attempt to encourage improvements we provide some guidelines for writing ABS application papers.

  • 4.
    Fricker, Samuel
    et al.
    Blekinge Institute of Technology, School of Computing.
    Persson, Marie
    Blekinge Institute of Technology, School of Computing.
    Larsson, Madelene
    Blekinge Institute of Technology, School of Computing.
    Tailoring the Software Product Management Framework for Use in a Healthcare Organization: Case Study2013Conference paper (Refereed)
    Abstract [en]

    Many reference models were developed for software process improvement. Each model, however, is an idealized prescription that is applicable in a lim-ited set of situation only. This paper has investigated how an existing refer-ence model can be tailored to a domain it has not been designed for initially. The tailoring approach is based on translating the reference model to the new domain and on inductive interviews for evaluating the translated model. The approach has been applied for assessing and improving strategic require-ments engineering practice in a healthcare organization with a framework for software product management.

  • 5.
    Holmgren, Johan
    et al.
    Malmo Univ., SWE.
    Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    An Optimization Model for Sequence Dependent Parallel Operating Room Scheduling2016In: HEALTH CARE SYSTEMS ENGINEERING FOR SCIENTISTS AND PRACTITIONERS, 2016, p. 41-51Conference paper (Refereed)
  • 6.
    Khambhammettu, Mahith
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Analyzing a Decision Support System for Resource Planning and Surgery Scheduling2016In: Procedia Computer Science / [ed] Martinho R.,Rijo R.,Cruz-Cunha M.M.,Bjorn-Andersen N.,Quintela Varajao J.E., Elsevier, 2016, Vol. 100, p. 532-538Conference paper (Refereed)
    Abstract [en]

    This study aims to propose a decision support system based on optimization modelling for operating room resource planning and sequence dependent scheduling of surgery operations. We conduct a simulation experiment using real world data collected from the local hospital to evaluate the proposed model. The obtained results are compared with real surgery schedules, planned at the local hospital. The experiment shows that the efficiency of schedules produced by the proposed model are significantly improved, in terms of less surgery turnover time, increased utilization of operating rooms and minimized make-span, compared to the real schedules. Moreover, the proposed optimization based decision support system enables analysis of surgery scheduling in relation to resource planning.

  • 7.
    Nordahl, Christian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Netz Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Monitoring Household Electricity Consumption Behaviour for Mining Changes2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an ongoing work on using a household electricity consumption behavior model for recognizing changes in sleep patterns. The work is inspired by recent studies in neuroscience revealing an association between dementia and sleep disorders and more particularly, supporting the hypothesis that insomnia may be a predictor for dementia in older adults. Our approach initially creates a clustering model of normal electricity consumption behavior of the household by using historical data. Then we build a new clustering model on a new set of electricity consumption data collected over a predefined time period and compare the existing model with the built new electricity consumption behavior model. If a discrepancy between the two clustering models is discovered a further analysis of the current electricity consumption behavior is conducted in order to investigate whether this discrepancy is associated with alterations in the resident’s sleep patterns. The approach is studied and initially evaluated on electricity consumption data collected from a single randomly selected anonymous household. The obtained results show that our approach is robust to mining changes in the resident daily routines by monitoring and analyzing their electricity consumption behavior model.

  • 8.
    Nordahl, Christian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Netz Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Profiling of household residents’ electricity consumption behavior using clustering analysis2019In: Lect. Notes Comput. Sci., Springer Verlag , 2019, p. 779-786Conference paper (Refereed)
    Abstract [en]

    In this study we apply clustering techniques for analyzing and understanding households’ electricity consumption data. The knowledge extracted by this analysis is used to create a model of normal electricity consumption behavior for each particular household. Initially, the household’s electricity consumption data are partitioned into a number of clusters with similar daily electricity consumption profiles. The centroids of the generated clusters can be considered as representative signatures of a household’s electricity consumption behavior. The proposed approach is evaluated by conducting a number of experiments on electricity consumption data of ten selected households. The obtained results show that the proposed approach is suitable for data organizing and understanding, and can be applied for modeling electricity consumption behavior on a household level. © Springer Nature Switzerland AG 2019.

  • 9.
    Nordahl, Christian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.2018In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis, https://sites.google.com/view/arial2018/accepted-papersprogram , 2018Conference paper (Refereed)
    Abstract [en]

    We propose a cluster analysis approach for organizing, visualizing and understanding households’ electricity consumption data. We initially partition the consumption data into a number of clusters with similar daily electricity consumption profiles. The centroids of each cluster can be seen as representative signatures of a household’s electricity consumption behaviors. We evaluate the proposed approach by conducting a number of experiments on electricity consumption data of ten selected households. Our results show that the approach is suitable for data analysis, understanding and creating electricity consumption behavior models.

  • 10. Persson, Marie
    Modelling and Analysing Hospital Surgery Operations2007Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    With an increasing proportion of elderly and an increasing demand for healthcare, managerial efforts are needed in order make the best use of resources and to keep cost under control. One of the most critical and expensive resources in a hospital is the operating theatre. This thesis aims to investigate the potential of computer-based modelling for supporting healthcare decision makers to improve management policies related to the hospital operating theatre. In a study conducted at a medium sized Swedish hospital we identify important prioritisations and decisions made in relation to patient scheduling and resource allocation when planning for surgery. Patient scheduling and operating room planning are complex tasks with a number of influencing factors to consider like, e.g., uncertainty in patient arrival, uncertainty in surgery procedure time and medical prioritisations and diagnosis. Further, several intersected dependencies, e.g. pre- and post operative care, have to be considered as to prevent occlusion and obtain a maximum patient through-put. With an optimisation-based approach we demonstrate how different criteria in patient scheduling and resource allocations can affect various objectives in terms of patient perspectives, staff perspectives and costs. For instance, we show that the current policy for resource allocation does not handle the variability generated by the patient diagnosis very well. In Sweden a law has recently been introduced, which advocates restrictions in elective patient waiting times. We extend the optimisation-based approach to include post-operative care and simulate a scenario based on patient data from a Swedish hospital to be able to predict the possible impact of the new law. The results indicate that the law causes an unsuitable increase in the waiting times for medium prioritised patients. Furthermore, we propose a combination of discreteevent simulation and optimisation to examine what impact different resource allocations of emergency and elective resources have on both utilisation rate and disturbance consequences, i.e. surgery cancellation and overtime work, due to emergency cases and other unexpected events. We show that both utilisation rate and cancellation frequencies can be improved significantly by the application of some minor changes in the resource allocation. Finally, we explore some future possibilities of using agent technology for modelling health care management decisions.

  • 11. Persson, Marie
    On the Improvement of Healthcare Management Using Simulation and Optimisation2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis concerns healthcare management and specifically addresses the problems of operating room planning and waiting list management. The operating room department is one of the most expensive areas within the healthcare system which necessitates many expensive resources such as staff, equipment and medicine. The planning of operating rooms is a complex task involving many dependencies and conflicting factors and hence careful operating room planning is critical to attain high productivity. One part of the planning process is to determine a Master Surgery Schedule (MSS). An MSS is a cyclic timetable that specifies the allocation of the surgical groups into different blocks of operating room time. Using an optimization-based approach, this thesis investigates whether the MSS can be adapted to better meet the varying surgery demand. Secondly, an extended optimization-based approach, including post-operative beds, is presented in which different policies related to priority rules are simulated to demonstrate their affect on the average waiting time. The problem of meeting the uncertainty in demand of patient arrival, as well as surgery duration, is then incorporated. With a combination of simulation and optimization techniques, different policies in reserving operating room capacity for emergency cases together with a policy to increase staff in stand-by, are demonstrated. The results show that, by adopting a certain policy, the average patient waiting time and surgery cancellations are decreased while operating room utilization is increased. Furthermore, the thesis focuses on how different aspects of surgery pre-conditions affect different performance measures related to operating room planning. The emergency surgery cases are omitted and the studies are delimited to concern the elective healthcare process only. With a proposed simulation model, an experimental tool is offered, in which a number of analyses related to the process of elective surgeries can be conducted. The hypothesis is that, sufficiently good estimates of future surgery demand can be assessed at the referral stage. Based on this assumption, an experiment is conducted to explore how different policies of managing incoming referrals affect patient waiting times. Related to this study, possibility of using data mining techniques to find indicators that can help to estimate future surgery demand is also investigated. Finally, in parallel, an agent-based simulation approach is investigated to address these types of problems. An agent-based approach would probably be relevant to consider when multiple planners are considered. In a survey, a framework for describing applications of agent based simulation is provided.

  • 12.
    Persson, Marie
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Hvitfeldt-Forsberg, Helena
    Medical Management Centre (MMC), SWE.
    Unbeck, Maria
    Danderyds sjukhus, SWE.
    Sköldenberg, Olof Gustaf
    Danderyds sjukhus, SWE.
    Stark, Andreas
    Danderyds sjukhus, SWE.
    Kelly-Pettersson, Paula
    Danderyds sjukhus, SWE.
    Mazzocato, Pamela
    Medical Management Centre (MMC), SWE.
    Operational strategies to manage non-elective orthopaedic surgical flows: A simulation modelling study2017In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 7, no 4, article id e013303Article in journal (Refereed)
    Abstract [en]

    Objectives To explore the value of simulation modelling in evaluating the effects of strategies to plan and schedule operating room (OR) resources aimed at reducing time to surgery for non-elective orthopaedic inpatients at a Swedish hospital. Methods We applied discrete-event simulation modelling. The model was populated with real world data from a university hospital with a strong focus on reducing waiting time to surgery for patients with hip fracture. The system modelled concerned two patient groups that share the same OR resources: hip-fracture and other non-elective orthopaedic patients in need of surgical treatment. We simulated three scenarios based on the literature and interaction with staff and managers: (1) baseline; (2) reduced turnover time between surgeries by 20â €..min and (3) one extra OR during the day, Monday to Friday. The outcome variables were waiting time to surgery and the percentage of patients who waited longer than 24â €..hours for surgery. Results The mean waiting time in hours was significantly reduced from 16.2â €..hours in scenario 1 (baseline) to 13.3â €..hours in scenario 2 and 13.6â €..hours in scenario 3 for hip-fracture surgery and from 26.0â €..hours in baseline to 18.9â €..hours in scenario 2 and 18.5â €..hours in scenario 3 for other non-elective patients. The percentage of patients who were treated within 24â €..hours significantly increased from 86.4% (baseline) to 96.1% (scenario 2) and 95.1% (scenario 3) for hip-fracture patients and from 60.2% (baseline) to 79.8% (scenario 2) and 79.8% (scenario 3) for patients with other non-elective patients. Conclusions Healthcare managers who strive to improve the timelines of non-elective orthopaedic surgeries may benefit from using simulation modelling to analyse different strategies to support their decisions. In this specific case, the simulation results showed that the reduction of surgery turnover times could yield the same results as an extra OR. © 2017 Published by the BMJ Publishing Group Limited.

  • 13. Persson, Marie
    et al.
    Lavesson, Niklas
    Identification of Surgery Indicators by Mining Hospital Data: A Preliminary Study2009Conference paper (Refereed)
    Abstract [en]

    The management of patient referrals is an interesting issue when it comes to predicting future patient demand to increase hospital productivity. In general, a patient is referred from the general practitioner to hospital care. A patient referral contains information that indicates the need for hospital care and this information is differently structured for different medical needs. In practice, these needs can be viewed as the forthcoming patient demand at the hospital, analogous to a volume of orders. Today, the structure of the referrals is very much up to the general practitioner who is referring the patient. This implies that the data provided to the hospital can vary extensively between cases. We suggest that, by enforcing a certain structure on the referral data, it may be possible to make early predictions about the patient demand. Such predictions could then be used as a basis for managing resources more efficiently to increase hospital productivity. This paper investigates the possibility of using data mining techniques to automatically generate prediction models by extracting conclusive information from patient records combined with surgical suite statistics, ,e.g., surgery preparations and anesthesia type, that are of significance for estimating patient demand in a surgery department, e.g., probability of surgery, surgery duration and recovery. We hypothesize that the generated models may provide new knowledge about, and a basis for, how to structure a patient referral. In addition, these models may also be used for the actual prediction of patient demand.

  • 14. Persson, Marie
    et al.
    Persson, Jan A.
    Analysing Management Policies for Operating Room Planning2007Conference paper (Refereed)
  • 15. Persson, Marie
    et al.
    Persson, Jan A.
    Analysing management policies for operating room planning using simulation2010In: Health Care Management Science, ISSN 1386-9620, E-ISSN 1572-9389, Vol. 13, no 2, p. 182-191Article in journal (Refereed)
    Abstract [en]

    In this paper we analyse the operating room planning at a department of orthopaedic surgery in Sweden. We focus on the problem of meeting the uncertainty in demand of patient arrival and surgery duration and at the same time maximizing the utilization of Operating Room (OR) time. With a discrete-event model we simulate how different management polices affect different performance metrics such as patient waiting time, cancellations and the utilization of OR time. The experiments show that the performance of the operating room department can be improved significantly by applying a different policy in reserving OR-capacity for emergency cases together with a policy to increase staff in stand-by. Moreover, the developed simulation model provides estimates for a what-if situation related to the prognosis of an increasing number of hip-joint replacements.

  • 16. Persson, Marie
    et al.
    Persson, Jan A.
    Health economic modeling to support surgery management at a Swedish hospital2009In: Omega - International Journal of Management Science, ISSN 0305-0483 , Vol. 37, no 4, p. 853-863Article in journal (Refereed)
    Abstract [en]

    Elective surgery management typically deals with a queue of patients that have to be scheduled for surgery within a certain time frame. considering both medical and economic constraints. In order to prevent the patient queue and waiting times from growing. surgery management has to decide whether to temporarily increase patient throughput at the regional hospital or have some patients scheduled for surgery at another hospital. In Sweden, a newly passed law states that patients who decide to receive surgery should not have to wait more than 90 days before this surgery is carried out. Therefore. if a patient decides to apply the new law by requesting Surgery within 90 days, the regional hospital is obliged to arrange and pay for either in-house Surgery or surgery at another hospital. In this paper, we Suggest ail approach using simulation including optimization for modeling surgery management decisions. We study a case based oil data from a General Surgery Department at a Swedish hospital and present Our results Lis a health economic evaluation. The results indicate ail increase in the mean waiting times for medium prioritized patient,, Mien the new law is applied

  • 17. Persson, Marie
    et al.
    Persson, Jan A.
    Health economical modelling to support surgery management at a Swedish hospital2006Conference paper (Refereed)
  • 18. Persson, Marie
    et al.
    Persson, Jan A.
    Optimisation Modelling of hospital operating room planning: analyzing strategies and problem settings2007Conference paper (Refereed)
    Abstract [en]

    There is a growing proportion of elderly which increases the demand for health care. As a consequence health care costs are rising and the need for hospital resource planning seems urgent. Different aspects (often conflicting) such as patient demand, clinical need and political ambitions must be considered. In this paper we propose a model for analyzing a hospital surgical suite with focus on operating room planning. An optimization model is developed for patient operation scheduling and for key resource allocation. Medical examinations and treatments of patients are performed using a number of resources, similar to products being refined in a number of processes in a logistics chain. Optimal resource allocation, given different objectives according to patient perspective, staff perspective, costs etc. under different system settings (e.g. principles for operating room allocation and amount of stand-by personnel), is studied. Preliminary results are presented based on case studies from two Swedish hospitals.

1 - 18 of 18
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