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  • 1.
    Andersen, Per Øivin
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Mobile-supported life charting for bipolar patients - user requirements study2013In: MEDINFO 2013: proceedings of the 14th World Congress on Medical and Health Informatics / [ed] Christoph Ulrich Lehmann, Elske Ammenwerth, Christian Nøhr, IOS Press, 2013, p. 1111-Conference paper (Other academic)
    Abstract [en]

    It is assumed that bipolar disorder patients can benefit from monitoring their mood, sleep, medicine intake and behavior which could be both done by patients themselves and in cooperation with health care professionals. This study aims at understanding what is required from a computerized system, as seen from the view of therapists and the patients, and how the newer mobile technologies (smart phones and tablets) can be utilized to support development of such a system. The study focuses on several existing solutions available either freely or on the market. Then these solutions are evaluated by both patients and medical professionals as a part of the system requirements study to be used in a new system development that will utilize mobile technologies to support the performance and patient outcomes.

  • 2.
    Antonsson, Johan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Quality of life using profile in coronary artery bypass surgery patients1999In: AMIA99,1999, Philadelphia: Hanley & Belfus Inc , 1999, p. 1013-Conference paper (Refereed)
  • 3.
    Antonsson, Johan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Kircher, Albert
    Technical University Graz Austria.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Uppsala .
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Design of a clinical decision support system for assist support devices in thoracic surgery2000In: AMIA,2000, Philadelphia: Hanley & Belfus Inc, , 2000Conference paper (Refereed)
  • 4.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Case Based Reasoningin Support of the LVAD Surgical Treatment2013In: Medicinteknikdagarna 2013, Electronic Proceedings, 2013Conference paper (Refereed)
  • 5.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knowledge discovery for advanced clinical data management and analysis1999In: Medical Informatics Europe 99,1999, Amsterdam: IOS Press , 1999, p. 409-Conference paper (Refereed)
  • 6.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Medical knowledge extraction: application of data analysis methods to support clinical decisions1993Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In building computer based clinical decision support extensive data analysis is sought to acquire all the medical knowledge needed to formulate the decision rules.

    This study explores, compares and discusses several approaches to knowledge extraction from medical data. Statistical methods (univariate, multivariate), probabilistic artificial intelligence approaches (inductive learning procedures, neural networks) and the rough sets were used for this purpose. The methods were applied in two clinical sets of data with well defined patients groups.

    The aim of the study was then to use different data analytical methods and extract knowledge, both of semantic and classification nature, enabling to differentiate among patients, observations and disease groups, what in turn was aimed to support clinical decisions.

    Semantic analysis was performed in two ways. In prior analysis subgroups or patterns were formed based on the distance within the data, while in posterior semantic analysis 'types' of observation falling into various groups and their measured values were explored.

    To study further discrimination, two empirical systems, based on principles of learning from examples, i.e. based on Quintan's ID3 algorithm (the AssPro system) and CART (Classification and Regression Trees), were compared. The knowledge representation in both systems is tree structured, thus the comparison is made according to the complexity, accuracy and structure of their optimal decision trees. The inductive learning system was additionaly compared and evaluated in relation to the location model of discriminant analysis, the linear Ficherian discrimination and the rough sets.

    All methods used were analysed and compared for their theoretical and applicative performances, and in some cases they were assessed medical appropriateness. By using them for the extensive knowledge extraction, it was possible to give a strong methodological basis for design of clinical decision support systems specific for the problem and the medical environments considered.

  • 7.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Medical knowledge extraction. Applications of data analysis methods1992Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis we explore and discuss some important methods for knowledge extraction from meclical data. This is done in relation to, and for the purpose of design and development of decision support systems, which could be population specific.

    To test data and extract knowledge, we use univariate and multivariate statistical methods, the rough sets theory and probabilistic artificial intelligence approaches. These methods are used to estimate characteristics of patient groups, disease profiles and other features relevant for medical problems. In particular, we apply them to clifferentiate among patient groups, develop patient models and derive decision rules. Our experience refers to two medical domains (patients with diagnosed and non-diagnosed, but suspected liver disease and patients with duodenal ulcer surgery).

    Extracted knowledge can be used both in clinical practice and health care programs, as well as in computer based decision support systems to adjust them to various clinical environments.

  • 8.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Site specific outcomes analysis: includingknowledge from a limited set of the cardiac assist support data1999In: Medical Informatics Europe99,1999, Amsterdam: IOSPres , 1999, p. 987-Conference paper (Refereed)
  • 9.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Univeristy of Bergen, Norway.
    The era of digital and electronic waste2014Conference paper (Other academic)
  • 10.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Mathiesen, Ulrik
    Oskarshamns sjukhus .
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Franzén, Lennart
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion, Pathology. Östergötlands Läns Landsting, Centre for Laboratory Medicine, Department of Clinical Pathology and Clinical Genetics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Machine learning to support diagnostics in the domain of asymptomatic liver disease1995In: MEDINFO95,1995, Edmonton: HC & CC , 1995, p. 809-Conference paper (Refereed)
  • 11.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Peeker, Martin
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Storm, Marcus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Thoraxkirurgi Uppsala.
    Casimir Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Case-based reasoning in a web-based clinical decision support system for thoracic surgery2002In: Am Medic Inform Ass Annual Symposium,2002, 2002, p. 968-968Conference paper (Refereed)
  • 12.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Hedin, Kristina
    Linköping University, Department of Molecular and Clinical Medicine.
    Mathiesen, Ulrik
    Oskarshamns sjukhus .
    Franzén, Lennart
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion, Pathology. Östergötlands Läns Landsting, Centre for Laboratory Medicine, Department of Clinical Pathology and Clinical Genetics.
    Frydén, Aril
    Linköping University, Department of Molecular and Clinical Medicine.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Decision support for monitoring of chronic Hepatitis C: can blood laboratory tests help?1996In: Medical Informatics Europe 96,1996, Amsterdam: IOS Press , 1996, p. 551-Conference paper (Refereed)
  • 13.
    Babic, Ankica
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Hiis Bergh, Fredrik
    Bjorgvin DPS, Helse Bergen HF, Norway.
    Rose Mari, Eikås
    Section for e-health, Helse Bergen, Norway.
    Grete, Mongstad
    National Association for the families of mentally ill, Bergen, Norway.
    Soerheim, Helen
    University of Bergen, Norway.
    Digi-Dag: Digital Diary for Users with Psychological  Disorders2013Conference paper (Other academic)
  • 14.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Koele, Werner
    Inst Biomed Engineering, Graz University Österike.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Lönn, Urban
    Dept of Cardio-Thoracic Surgery, Uppsala Universiet.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Help and advisory system in a Web-based system for data mining2001In: AMIA 2001,2001, Washington: Hanley&Belfus , 2001, p. 856-Conference paper (Refereed)
  • 15.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Krusinska, Ewa
    University of Wrocslaw .
    Koren, Iztok
    Faculty of Electrical and Computer Engineering Ljubljana.
    Gyergyek, Ludvik
    Faculty of Electrical and Computer Engineering Ljubljana.
    Semantic modelling of biomedical data1991In: International Symposium on Biomedical Engineering,1991, 1991, p. 282-Conference paper (Refereed)
  • 16.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Krusinska, Ewa
    IMT LiU.
    Strömberg, Jan-Erik
    Dept Electrical Engineering LiU.
    Extraction of diagnostic rules using recursive partitioning systems: A comparision of two approaches1992In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 4, p. 373-387Article in journal (Refereed)
  • 17.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Linköping Heart Center Linköping University.
    Peterzén, Bengt
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Anaesthesiology. Östergötlands Läns Landsting, Anaesthesiology and Surgical Centre, Department of Intensive Care UHL.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Hemopump treatment in patients with postcardiotomy heart failure1995In: Annals of Thoracic Surgery, ISSN 0003-4975, E-ISSN 1552-6259, Vol. 60, p. 1067-1071Article in journal (Refereed)
  • 18.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Maojo, Victor
    University of Madrid Spain.
    Martin-Sanchez, Fernando
    Inst of Health Carlos I Madrid Spain.
    Santos, Miguel
    University of Aveiro Portugal.
    Sousa, Antonio
    University of Aveiro Portugal.
    The INFOGENMED project: A Biomedical informatics approach to integrate heterogeneous biological and clinical information2005In: ERCIM news, ISSN 1564-0094, Vol. 60, no JanuaryArticle in journal (Refereed)
  • 19.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Mathiesen, Ulrik
    Oskarshamn County Hospital Sweden.
    Hedin, Kristina
    Linköping University, Department of Molecular and Clinical Medicine.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Assessing an AI knowledge-Base for asymptomatic liver diseases1998In: AMIA98,1998, Philadelphia: Hanley & Belfuse , 1998, p. 513-Conference paper (Refereed)
  • 20.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Olivier, José Luis
    University of Aveiro, Portugal.
    Voznuka, Natalja
    Linköpings universitet.
    Oliviera, Ilidio
    University of Aveiro, Portugal.
    Storm, Markus
    Linköpings universitet.
    Maojo, Victor
    Universidad Politecnica de Madrid, Spain.
    Sanchez, Fernando
    Instituto de Salud Carlos III, Spain.
    Santos, Miguel
    Genomica STAB VIDA, Portugal.
    Pereira, Antonio Sousa
    University of Aveiro, Portugal.
    Confidentiality and security issues in web services managing patient clinical and genetic data2004Report (Other academic)
  • 21.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Petelin, Milan
    University of Ljubljana .
    Ivanusa, Teodora
    University of Ljubljana .
    Convergen assessment of radiographic diagnostic systems1997In: IEEE Symposium on Computer-Based Medical Systemss,1997, Washington: IEEE Computer , 1997, p. 205-Conference paper (Refereed)
  • 22.
    Babic, Ankica
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Peterzen, Bengt
    Östergötlands Läns Landsting, Heart and Medicine Center.
    Lönn, Urban
    Östergötlands Läns Landsting, Heart and Medicine Center.
    Casimir Ahn, Henrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Thoracic and Vascular Surgery.
    Case Based Reasoning in a Web Based Decision Support System for Thoracic Surgery2013In: IFMBE Proceedings 41 / [ed] L.M. Roa Romero, Springer, 2013, p. 1413-1416Conference paper (Refereed)
    Abstract [en]

    Case Based Reasoning (CBR) methodology provides means of collecting patients cases and retrieving them following the clinical criteria. By studying previously treated patients with similar backgrounds, the physician can get a better base for deciding on treatment for a current patient and be better prepared for complications that might occur during and after surgery. This could be taken advantage of when there is not enough data for a statistical analysis, but electronic patient records that provide all the relevant information to assure a timely and accurate clinical insight into a patient particular situation.

    We have developed and implemented a CBR engine using the Nearest Neighbor algorithm. A patient case is represented as a combination of perioperative variable values and operation reports. Physicians could review a selected number of cases by browsing through the electronic patient record and operational narratives which provides an exhaustive insight into the previously treated cases. An evaluation of the search algorithm suggests a very good functionality.

  • 23.
    Babic, Ankica
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Soerheim, Helen
    University of Bergen, Norway.
    M-Health ApplicationProduct Development for Physiological Disorders Based on Interaction Design2013In: Medicinteknikdagarna 2013, Electronic Proceedings, 2013Conference paper (Refereed)
  • 24.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ster, Branko
    Computer and Inforamtion Science University of Ljubljana.
    Pavesic, Nikola
    Electrical Engineering University of Ljubljana.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Machine Learning for the quality of life in inflammatory bowel disease1997In: Medical Informatics Europe97,1997, Amsterdam: IOS Press , 1997, p. 661-Conference paper (Refereed)
  • 25.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Zganec, Mario
    University of Ljubljana .
    Palcic, Branko
    Cancer Research Centre BC Canada.
    3D presentation of the nuclear cell features in quantitative cytometry1996In: AMIA 1996,1996, Washington: Hanley & Belfus , 1996, p. 679-Conference paper (Refereed)
  • 26.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Mathiesen, Ulrik
    Oskarshamn Hospital .
    Artificial neural networks in clustering and classification of data on unspecified liver diseases1993In: Nordic Meeting on Medical and Biomeidical engineering,1993, 1993, p. 136-Conference paper (Refereed)
  • 27.
    Berg Andersen, Per
    et al.
    University of Bergen, Norway .
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway .
    Self-reporting for Bipolar Patients through Smartphone2014In: IFMBE Proceedings / [ed] Laura M. Roa Romero, Springer, 2014, Vol. 41, p. 1358-1361Conference paper (Refereed)
    Abstract [en]

    Self-reporting of symptoms is widely used and validated in the field of psychiatry, also in the context of bipolar disorder. This paper presents work on a self-reporting system for bipolar patients using a smartphone to gather data from the patient, which is communicated to a server via a secure connection. The data is presented in a web application to a patient for his/hers self-monitoring, and to medical personnel associated with the treatment of the patient. The work described here is part of an ongoing system development and gives insights into the field research and motivation for choosing Life Charting Methodology as a structural element. Leaning on such well accepted and validated therapeutic tools should secure validity and feasibility of the final system that would appear to patients as familiar and easy to use. Consequently, the application is expected to be directly understandable to everyone involved in the treatment. Programming solutions will capture the essence, but will be adjusted to the electronic environment which will be validated for its correctness and user-friendliness.

  • 28.
    Bergquist, Urban
    et al.
    Inst för medicinsk teknik Linköpings universitet.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Aspects of certainty in patient classification using a Health-related Quality-of-Life instrument in inflammatory bowel disease1999In: AMIA99,1999, Philadelphia: Hanley & Belfus Inc , 1999, p. 202-Conference paper (Refereed)
  • 29.
    Berntsen, Eirik
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Cherry: mobile application for children with cancer2013In: MEDINFO 2013: proceedings of the 14th World Congress on Medical and Health Informatics / [ed] Christoph Ulrich Lehmann, Elske Ammenwerth, Christian Nøhr, IOS Press, 2013, p. 1168-Conference paper (Other academic)
    Abstract [en]

    The Cherry project seeks to address the information needs of young cancer patients, their parents, and health care providers. It aims at helping the patients to understand various aspects of their disease and treatment, and allow them to assess and record their disease related quality of life. It uses elements of social media to offer a meeting point with the physician and peers. Information is presented in a way that is both understandable and appealing to young children in school age and adolescents. Preschool children will be studied as a separate user group to address their needs and possibilities to meet them. The Cherry system wants to utilize Internet and mobile technologies to benefit patient outcome.

  • 30.
    Berntsen, Eirik
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Univeristy of Bergen, Norway.
    Information System for Postmarket Surveillance of Total Joint Prostheses2015In: 16th Nordic-Baltic Conference on Biomedical Engineering / [ed] Henrik Mindedal ; Mikael Persson, Springer, 2015, p. 24-27Conference paper (Refereed)
    Abstract [en]

    Storage, integration and presentation of clinical data is an important aspect of any modern medical research. The Biomaterials research group at the Haukeland University Hospital uses both their own locally generated clinical data and external registry data to examine explanted joint implants. As a solution to this challenge, a system prototype was developed that would enable further integration of these information systems into a multi-user environment.

    The system allows importing registry data and matching it with local data, viewing and editing of this information and exporting the integrated data for further statistical analysis. An evaluation consisting of both user testing and heuristic evaluation was carried out and generated constructive feedback.

    The prototype demonstrates the feasibility of combining these data sources in a single database and the future possibility of exposing parts of this information to external users through a web application.

    Future integration of external sources could improve the information management of biobank data for postmarket surveillance of medical devices.

  • 31.
    Chowdhury, Shamsul
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Haug, Peter
    Utah University USA.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Methods for knowledge extraction from clinical database on liver diseases1991In: Computers and biomedical research, ISSN 0010-4809, E-ISSN 1090-2368, Vol. 24, p. 530-548Article in journal (Refereed)
  • 32.
    Dahlström, Örjan
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Antonsson, Johan
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lönn, Urban
    Uppsala Universitet.
    Ahn, Henrik Casimir
    Linköping University, Department of Medicine and Care, Thoracic Surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Clustering as a data mining method in a Web-based system for thoracic surgery2001In: Journal of the Medical Informatics Association. Symposium Supplement, Washington: Hanley&Belfus , 2001, p. 888-Conference paper (Refereed)
    Abstract [en]

    Cluster analysis is one way of data mining from large amounts of information. Being able to perform series of analyses, varying clinical criteria and requests, expected results of the clustering might be truly rewarding. Instead of having a few hypotheses prepared and tested, medical experts can be surprised by obtaining a set of hypotheses to further validate and work on.

    Internet technologies enable a substantial flexibility that can be taken advantage of when implementing a Web-based tool. Division of Medical Informatics together with Linkoping Heart Center of the Linkoping University is developing procedures for multivariate clustering within the Web-based AssistMe1 system.

  • 33.
    Fjellanger, Maiken Beate
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Univeristy of Bergen, Norway.
    Digital storytelling as a tool for conveying cancer diagnoses to children2014In: MTD Abstract Proceedings, Medicinteknikdagarna Göteborg, 14-16 oktober, 2014, 2014Conference paper (Refereed)
    Abstract [en]

    The experience of receiving a diagnosis of a life-threatening illness will be difficult for many, especially for children as they often have inadequate knowledge and understanding of what this entails (Fottland, 2004). It is therefore important that they receive thorough and accurate information about the disease together with the diagnosis, and that this information is presented in a child -friendly way. This is the essence of this project. The type of diagnosis chosen for this project is cancer, as research shows that this diagnosis evokes difficult emotions for many children (Fottland, 2004). According to Fottland (2004) many children have the perception that cancer implies death.

    The project goal is to create a digital storytelling tool that presents a story of a child that gets a cancer diagnosis and how the story main character experiences it, as well as what is happening in the body as the treatment develops. This way children will learn about the emotional as well as the medical aspects of the disease. The project has two focus areas; a psychological to facilitate the story-telling in a child-friendly learning way, as well as a technical with focus on interaction design.

  • 34. Garcia, Remesal M.
    et al.
    Maojo, V.
    Billhardt, H.
    Crespo, J.
    Alonso, Calvo R.
    Perez, D.
    Martin-Sanchez, F.
    Pereira, Antonio Sousa
    University of Aveiro, Portugal.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    ARMEDA II: Integrated access to heterogeneous biomedical databases2004In: medinfo- World Congress on Medical Informatics,2004, Washington: Elsevier Science Publ. , 2004, p. 1607-Conference paper (Refereed)
  • 35.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Science & Engineering.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Department of Information Science and Media Studies, University of Bergen, Norway.
    A pattern recognition framework for detecting dynamic changes on cyclic time series2015In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 3, p. 696-708Article in journal (Refereed)
    Abstract [en]

    This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed  model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.

  • 36.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lindèn, Maria
    Mälardalen University, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Univeristy of Bergen, Norway.
    A Novel Model for Screening Aortic Stenosis Using Phonocardiogram2015In: 16th Nordic-Baltic Conference on Biomedical Engineering / [ed] Henrik Mindedal and Mikael Persson, Springer Science Business Media , 2015, p. 48-51Conference paper (Refereed)
    Abstract [en]

    This study presents an algorithm for screening aortic stenosis, based on heart sound signal processing. It benefits from an artificial intelligent-based (AI-based) model using a multi-layer perceptron neural network. The AI-based model learns disease related murmurs using non-stationary features of the murmurs. Performance of the model is statistically evaluated using two different databases, one of children and the other of elderly volunteers with normal heart condition and aortic stenosis. Results showed a 95% confidence interval of the high accuracy/sensitivity (84.1%-86.0%)/(86.0%-88.4%) thus exhibiting a superior performance to a cardiologist who relies on the conventional auscultation. The study suggests including the heart sound signal in the clinical decision making due to its potential to improve the screening accuracy.

  • 37.
    Granfeldt, Hans
    et al.
    Linköping University, Department of Medical and Health Sciences, Thoracic Surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Bansi, Bansi
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences.
    Wiklund, Lars
    University Hospital, Lund, Sweden.
    Peterzén, Bengt
    Linköping University, Department of Medical and Health Sciences, Vascular surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Lönn, Urban
    University Hospital, Gothenburg, Sweden.
    Babic, Ankica
    University Hospital, Uppsala, Sweden.
    Ahn, Henrik
    Linköping University, Department of Medicine and Care, Vascular surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Risk Factor Analysis of Swedish Left Ventricular Assist Device (LVAD) Patients2003In: Annals of Thoracic Surgery, ISSN 0003-4975, E-ISSN 1552-6259, Vol. 76, no 6, p. 1993-1998Article in journal (Refereed)
    Abstract [en]

    Background. The use of left ventricular assist devices (LVADs) is established as a bridge to heart transplantation. Methods. All Swedish patients on the waiting list for heart transplantation, treated with LVAD since 1993 were retrospectively collected into a database and analyzed in regards to risk factors for mortality and morbidity. Results. Fifty-nine patients (46 men) with a median age of 49 years (range, 14 to 69 years), Higgins score median of 9 (range, 3 to 15), EuroScore median of 10 (range, 5 to 17) were investigated. Dominating diagnoses were dilated cardiomyopathy in 61% (n = 36) and ischemic cardiomyopathy in 18.6% (n = 11). The patients were supported with LVAD for a median time of 99.5 days (range, 1 to 873 days). Forty-five (76%) patients received transplants, and 3 (5.1%) patients were weaned from the device. Eleven patients (18.6%) died during LVAD treatment. Risk factor analysis for mortality before heart transplantation showed significance for a high total amount of autologous blood transfusions (p < 0.001), days on mechanical ventilation postoperatively (p < 0.001), prolonged postoperative intensive care unit stay (p = 0.007), and high central venous pressure 24 hours postoperatively and at the final measurement (p = 0.03 and 0.01, respectively). Mortality with LVAD treatment was 18.6% (n = 11). High C-reactive protein (p = 0.001), low mean arterial pressure (p = 0.03), and high cardiac index (p = 0.03) preoperatively were risk factors for development of right ventricular failure during LVAD treatment. Conclusions. The Swedish experience with LVAD as a bridge to heart transplantation was retrospectively collected into a database. This included data from transplant and nontransplant centers. Figures of mortality and morbidity in the database were comparable to international experience. Specific risk factors were difficult to define retrospectively as a result of different protocols for follow-up among participating centers. © 2003 by The Society of Thoracic Surgeons.

  • 38.
    Hassling, Linda
    et al.
    Inst medicinsk teknik Linköpings universitet.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Jönsson, Arne
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Lönn, Urban
    Dept Cardio-Thoracic surgery Uppsala universitet.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Assessing patient information needs as a part of man-machine dialogue development2001In: AMIA2001,2001, Washington: Hanley&Belfus , 2001, p. 922-Conference paper (Refereed)
  • 39.
    Hassling, Linda
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    A web-based patient information system - identification of patients' information needs2003In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 27, no 3, p. 247-257Article in journal (Refereed)
  • 40.
    Hassling, Linda
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Thoraxkirurgi, Akademiska sjukhuset Uppsala.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Assessment of patient information needs for a health information system in thoracic surgery and care.2002In: Health Care MMII,2002, 2002, p. 41-41Conference paper (Other academic)
  • 41. Hedin, K
    et al.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Frydén, Aril
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases . Östergötlands Läns Landsting, Centre for Medicine, Department of Infectious Diseases in Östergötland.
    Liver guide for monitoring of chronic hepatitis C2000In: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, p. 340-343Article in journal (Refereed)
    Abstract [en]

    The severity of chronic hepatitis C infection in the Individual patient is monitored using blood laboratory findings and liver biopsy. Lf blood test results could be shown to provide sufficient information concerning the disease, the invasive procedure of liver biopsy could perhaps be avoided in some instances. This study assessed the clinical relevance of blood laboratory tests for detecting disease-related changes. in the liver. Histopathological classification was used ta assign class membership of the patients and data mining operations were performed in an elaborate way on 19 different data sets. Disease activity could be detected by a small set of blood tests. Extended sets could identify more severe changes, but failed to distinguish them. The extracted rules are implemented as a part of the knowledge base of a corresponding decision support system aimed at specialists and general practitioners.

  • 42. Hedin, K
    et al.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Frydén, Aril
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases . Östergötlands Läns Landsting, Centre for Medicine, Department of Infectious Diseases in Östergötland.
    Take care: Patient oriented information system regarding chronic hepatitis C1999In: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, p. 1075-1075Conference paper (Other academic)
  • 43.
    Hedin, Kristina
    et al.
    Linköping University, Department of Molecular and Clinical Medicine.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Frydén, Aril
    Linköping University, Department of Molecular and Clinical Medicine.
    Take care: Guidelines for patients with chronic Hepatitis C1999In: Medical Informatics Europe99,1999, Amsterdam: IOS Press , 1999, p. 783-Conference paper (Refereed)
  • 44.
    Hedin, Kristina
    et al.
    Linköping University, Department of Molecular and Clinical Medicine.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Frydén, Aril
    Linköping University, Department of Molecular and Clinical Medicine.
    Take CAre: Patient-orientedinformation system regarding chronic hepatitis C1999In: Medical Informatics Europe99,1999, Amsterdam: IOS Press , 1999, p. 1075-Conference paper (Refereed)
  • 45.
    Ivanusa, Teodora
    et al.
    Veterinary Faculty University of Ljubljana.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Diagnostic ssytems for assessing alveolar bone loss1997In: Medical Informatcs Europe 97,1997, Amsterdam: IOS Press , 1997, p. 478-Conference paper (Refereed)
  • 46.
    Ivanusa, Teodora
    et al.
    Veterinary Faculty, University of Ljubljana .
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Monitoring of alveolar bone loss in experimental periodonitis in dogs1998In: Word Veterinary Dental Congress 97,1997, 1998, p. 43-Conference paper (Refereed)
  • 47.
    Jonsson, Jens
    et al.
    Inst för medicinsk teknik Linköpigs universitet.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Design features for internet-based quality of life instrument in inflammatory bowel disease1999In: AMIA99,1999, Philadelphia: Hanley & Belfus Inc , 1999, p. 1092-Conference paper (Refereed)
  • 48.
    Jonsson, Jens
    et al.
    IMT LIU.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Quantitative collagen as a golden standard in different diagnosing of fibotic changes in liver tissue1999In: Medical Informatics Europe99,1999, Amsterdam: IOS Press , 1999, p. 749-Conference paper (Refereed)
  • 49.
    Kanza, G.
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Data Mining in Cancer Registries: A Case for Design Studies2013In: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 / [ed] L.M. Roa Romero, Springer, 2013, p. 1417-1420Conference paper (Refereed)
    Abstract [en]

    Cancer registries are created, managed and data mined to gain knowledge about long term patient outcomes, effects of medication, clinical factors influencing patients’ wellbeing. Equally important is the insight into the cost effectiveness of cancer treatments, and securing data input from different medical centers and enable competent data analysis and meaningful results. Interest among different user groups (physicians, researchers, health care administrators, policy makers) cerates expectations regarding the results and active role in the development and in interactive use of the information. This paper discusses several design cases in which data mining could be implemented to enable efficient and user friendly knowledge extraction. Three important design cases have been identified following the pathways that the users typically make: 1. ensemble data mining from long term national registries; 2. ensemble data mining form the dedicated clinical web-databases; 3. ensemble distributed data mining and analysis.

  • 50.
    Kanza, Grace
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Univeristy of Bergen, Norway.
    Agile Development for Smart User Interfaces to Cancer Registries2015Conference paper (Refereed)
    Abstract [en]

    The paper studies different user interfaces developed for visualizing data mined from cancer registries. The motivation behind this research is a need to create more flexible and smart, easy to use interfaces that will assist users in utilizing and exploring clinical information. The design process combined Parallel and Iterative design process models together with Personal Kanban for managing the development process. The developed prototype provides users with a choice of several data visualization possibilities, depending on the user’s tasks and goals. A preliminary user interface was evaluated resulting in recommendations for further development. Heuristic evaluation with potential experts had scores on all the high scores on all the design dimensions: H1=visibility of system status; H2=match between the system and the real world; H3=user control and freedom; H4=consistency and standards; H5=error prevention; H6=recognition rather than recall; H7=flexibility and efficiency of use; H8=aesthetic and minimalist design; H9=help users recognize, diagnose, and recover from errors; and H10=help and documentation.

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