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Netz Persson, Marie
Alternative names
Publications (10 of 18) Show all publications
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2019). Monitoring Household Electricity Consumption Behaviour for Mining Changes. In: : . Paper presented at 3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China..
Open this publication in new window or tab >>Monitoring Household Electricity Consumption Behaviour for Mining Changes
2019 (English)Conference paper, Oral presentation only (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.

National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18651 (URN)
Conference
3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China.
Projects
Scalable resource-efficient systems for big data analytics
Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-10-17Bibliographically approved
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2019). Profiling of household residents’ electricity consumption behavior using clustering analysis. In: Lect. Notes Comput. Sci.: . Paper presented at International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019 (pp. 779-786). Springer Verlag
Open this publication in new window or tab >>Profiling of household residents’ electricity consumption behavior using clustering analysis
2019 (English)In: Lect. Notes Comput. Sci., Springer Verlag , 2019, p. 779-786Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Series
Lecture Notes in Computer Science ; 11540
Keywords
Ambient Assisted Living, Non-intrusive remote monitoring, Assisted living, Clustering analysis, Clustering techniques, Electricity-consumption, Household level, Number of clusters, Remote monitoring, Electric power utilization
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18593 (URN)10.1007/978-3-030-22750-0_78 (DOI)2-s2.0-85068459816 (Scopus ID)9783030227494 (ISBN)
Conference
International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-17Bibliographically approved
Nordahl, C., Grahn, H., Persson, M. & Boeva, V. (2018). Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.. In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis: . Paper presented at 2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18, Stockholm. https://sites.google.com/view/arial2018/accepted-papersprogram
Open this publication in new window or tab >>Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.
2018 (English)In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis, https://sites.google.com/view/arial2018/accepted-papersprogram , 2018Conference paper, Published 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.

Place, publisher, year, edition, pages
https://sites.google.com/view/arial2018/accepted-papersprogram, 2018
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-17439 (URN)
Conference
2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18, Stockholm
Projects
BigData@BTH
Available from: 2018-12-19 Created: 2018-12-19 Last updated: 2019-01-16Bibliographically approved
Persson, M., Hvitfeldt-Forsberg, H., Unbeck, M., Sköldenberg, O. G., Stark, A., Kelly-Pettersson, P. & Mazzocato, P. (2017). Operational strategies to manage non-elective orthopaedic surgical flows: A simulation modelling study. BMJ Open, 7(4), Article ID e013303.
Open this publication in new window or tab >>Operational strategies to manage non-elective orthopaedic surgical flows: A simulation modelling study
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2017 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 7, no 4, article id e013303Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
BMJ Publishing Group, 2017
Keywords
efficiency, operating room, orthopaedic surgery, Quality improvement, simulation modelling
National Category
Surgery
Identifiers
urn:nbn:se:bth-14131 (URN)10.1136/bmjopen-2016-013303 (DOI)000402527200032 ()2-s2.0-85017322814 (Scopus ID)
Note

open access

Available from: 2017-04-28 Created: 2017-04-28 Last updated: 2017-11-29Bibliographically approved
Holmgren, J. & Persson, M. (2016). An Optimization Model for Sequence Dependent Parallel Operating Room Scheduling. In: HEALTH CARE SYSTEMS ENGINEERING FOR SCIENTISTS AND PRACTITIONERS: . Paper presented at 2nd International Conference on Health Care Systems Engineering (HCSE), Lyon, FRANCE (pp. 41-51).
Open this publication in new window or tab >>An Optimization Model for Sequence Dependent Parallel Operating Room Scheduling
2016 (English)In: HEALTH CARE SYSTEMS ENGINEERING FOR SCIENTISTS AND PRACTITIONERS, 2016, p. 41-51Conference paper, Published paper (Refereed)
Series
Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009 ; 169
National Category
Mathematics Other Medical Sciences
Identifiers
urn:nbn:se:bth-13891 (URN)10.1007/978-3-319-35132-2_5 (DOI)000391871300005 ()978-3-319-35132-2 (ISBN)
Conference
2nd International Conference on Health Care Systems Engineering (HCSE), Lyon, FRANCE
Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2017-02-24Bibliographically approved
Khambhammettu, M. & Persson, M. (2016). Analyzing a Decision Support System for Resource Planning and Surgery Scheduling. In: Martinho R.,Rijo R.,Cruz-Cunha M.M.,Bjorn-Andersen N.,Quintela Varajao J.E. (Ed.), Procedia Computer Science: . Paper presented at Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist, Porto (pp. 532-538). Elsevier, 100
Open this publication in new window or tab >>Analyzing a Decision Support System for Resource Planning and Surgery Scheduling
2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Elsevier, 2016
Series
Procedia Computer Science, ISSN 1877-0509
Keywords
Decision support system, Healthcare, Operating rooms, Optimization, Scheduling, Turnover time, Artificial intelligence, Decision support systems, Health care, Hospitals, Information systems, Project management, Resource allocation, Surgery, Optimization modelling, Real surgeries, Real-world, Resource planning, Sequence-dependent, Information management
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Other Computer and Information Science
Identifiers
urn:nbn:se:bth-13766 (URN)10.1016/j.procs.2016.09.192 (DOI)000392695900065 ()2-s2.0-85006942544 (Scopus ID)
Conference
Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist, Porto
Available from: 2017-01-16 Created: 2017-01-16 Last updated: 2018-01-13Bibliographically approved
Beyene, A. A., Welemariam, T., Persson, M. & Lavesson, N. (2015). Improved concept drift handling in surgery prediction and other applications. Knowledge and Information Systems, 44(1), 177-196
Open this publication in new window or tab >>Improved concept drift handling in surgery prediction and other applications
2015 (English)In: Knowledge and Information Systems, ISSN 0219-1377, Vol. 44, no 1, p. 177-196Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2015
Keywords
online learning, incremental learning, machine learning, concept drift, BigData@BTH
National Category
Computer Sciences Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:bth-6694 (URN)10.1007/s10115-014-0756-9 (DOI)000356297500008 ()oai:bth.se:forskinfo93F9CDEFE847C5E2C1257CED001F2DE7 (Local ID)oai:bth.se:forskinfo93F9CDEFE847C5E2C1257CED001F2DE7 (Archive number)oai:bth.se:forskinfo93F9CDEFE847C5E2C1257CED001F2DE7 (OAI)
Note

http://link.springer.com/article/10.1007%2Fs10115-014-0756-9

Available from: 2014-06-05 Created: 2014-06-04 Last updated: 2018-01-11Bibliographically approved
Dasari, S. K., Lavesson, N., Andersson, P. & Persson, M. (2015). Tree-Based Response Surface Analysis. In: : . Paper presented at The International Workshop on Machine learning, Optimization and big Data (MOD 2015), Taormina - Sicily, Italy (pp. 118-129). Springer, 9432
Open this publication in new window or tab >>Tree-Based Response Surface Analysis
2015 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer, 2015. p. 12
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9432
Keywords
Machine learning, Regression, Surrogate model, Response surface model
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-11442 (URN)10.1007/978-3-319-27926-8_11 (DOI)978-3-319-27925-1 (ISBN)
Conference
The International Workshop on Machine learning, Optimization and big Data (MOD 2015), Taormina - Sicily, Italy
Funder
Knowledge Foundation
Available from: 2016-01-19 Created: 2016-01-19 Last updated: 2019-04-25Bibliographically approved
Fricker, S., Persson, M. & Larsson, M. (2013). Tailoring the Software Product Management Framework for Use in a Healthcare Organization: Case Study. In: : . Paper presented at EuroSPI. Dundalk, Ireland: Springer
Open this publication in new window or tab >>Tailoring the Software Product Management Framework for Use in a Healthcare Organization: Case Study
2013 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
Dundalk, Ireland: Springer, 2013
Keywords
reference model tailoring, inductive process improvement
National Category
Business Administration Human Aspects of ICT Software Engineering
Identifiers
urn:nbn:se:bth-6916 (URN)oai:bth.se:forskinfoBE1B9CAFB861117EC1257BC6001D07C2 (Local ID)978-3-642-39178-1 (ISBN)oai:bth.se:forskinfoBE1B9CAFB861117EC1257BC6001D07C2 (Archive number)oai:bth.se:forskinfoBE1B9CAFB861117EC1257BC6001D07C2 (OAI)
Conference
EuroSPI
Available from: 2013-08-29 Created: 2013-08-13 Last updated: 2018-01-11Bibliographically approved
Persson, M. & Persson, J. A. (2010). Analysing management policies for operating room planning using simulation. Health Care Management Science, 13(2), 182-191
Open this publication in new window or tab >>Analysing management policies for operating room planning using simulation
2010 (English)In: Health Care Management Science, ISSN 1386-9620, E-ISSN 1572-9389, Vol. 13, no 2, p. 182-191Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2010
Keywords
Healthcare, Management, Operating room planning, Optimisation, Simulation
National Category
Software Engineering Nursing
Identifiers
urn:nbn:se:bth-7773 (URN)10.1007/s10729-009-9122-y (DOI)000281585300007 ()oai:bth.se:forskinfoA0779A96C9E39F6EC12577580042CE05 (Local ID)oai:bth.se:forskinfoA0779A96C9E39F6EC12577580042CE05 (Archive number)oai:bth.se:forskinfoA0779A96C9E39F6EC12577580042CE05 (OAI)
Available from: 2012-09-18 Created: 2010-07-06 Last updated: 2018-01-11Bibliographically approved
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