liu.seSearch for publications in DiVA
Change search
Refine search result
12 1 - 50 of 93
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 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.
    Aserod, Hanne
    et al.
    Univ Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Designing a mobile system for safety reporting of arthroplasty adverse events2018In: EMBEC and NBC 2017, SPRINGER-VERLAG SINGAPORE PTE LTD , 2018, Vol. 65, p. 571-574Conference paper (Refereed)
    Abstract [en]

    This paper presents a mobile software application development for safety reporting of adverse events within the field of arthroplasty. Proposed user interface enables entry of data specific for adverse events of the knee and hip implants. Besides the patient data, the system supports entry of the event, its classification (serious, non-serious), its follow up, as well as a connection to the database maintained within the Helse Bergen hospital information system. Safety reports can be initiated and retrieved on request and depending on the adjudication of the event; suspected severe events should be followed up until their resolution. Expert evaluation of the first design solution was performed using low fidelity prototype. It has shown that design was relevant, straightforward, done in a way that official reporting would commence. Some users were positive to the reporting, some felt it would demand more work. A comprehensive evaluation with different potential user groups is planned to meet their needs and understand their views.

  • 5.
    Aserod, Hanne
    et al.
    Univ Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Pharmacovigilance Mobile Tool Design in the Field of Arhroplasty2017In: INFORMATICS EMPOWERS HEALTHCARE TRANSFORMATION, IOS PRESS , 2017, Vol. 238, p. 104-107Conference paper (Refereed)
    Abstract [en]

    Pharmacovigilance is an important part of the patient safety and it has a great appeal to physicians. It is concerned with the safety of medical devices and treatments in the light of understanding the risks and dangers based on the already reported safety issues. Internet resources such as the Manufacturer And User Facility Device Experience (MAUDE) web-site are often retrieved due to the lack of internal, local safety databases. The research looked at how Human Computer Interaction could improve user experience. We have designed data entry for safety reporting and pharmacovigilance based on the web-bases system called WebBISS (Web-based implant search system). The expectation is not only to improve usability, but also to stimulate physicians to enter their safety data and become also contributors, and not only users of information. The expert evaluation has been generally positive and encouraged stronger help and error reporting functions. The high fidelity design has given a good impression of the future mobile solution.

  • 6.
    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)
  • 7.
    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)
  • 8.
    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.

  • 9.
    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.

  • 10.
    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)
  • 11.
    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)
  • 12.
    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)
  • 13.
    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)
  • 14.
    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)
  • 15.
    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)
  • 16.
    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)
  • 17.
    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)
  • 18.
    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)
  • 19.
    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)
  • 20.
    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)
  • 21.
    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)
  • 22.
    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)
  • 23.
    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)
  • 24.
    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.

  • 25.
    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)
  • 26.
    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)
  • 27.
    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)
  • 28.
    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)
  • 29.
    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.

  • 30.
    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)
  • 31.
    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.

  • 32.
    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.

  • 33.
    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)
  • 34.
    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.

  • 35.
    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.

  • 36. 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)
  • 37.
    Gesicho, Milka B.
    et al.
    Department of Information Science and Media Studies, University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Were, Martin C.
    Institute of Biomedical Informatics, Moi University, Kenya.
    Critical Issues in Evaluating National-Level Health Data Warehouses in LMICs: Kenya Case Study2017In: Informatics Empowers Healthcare Transformation / [ed] Househ M.S.,Mantas J.,Hasman A.,Gallos P., 2017, Vol. 238, p. 201-204Conference paper (Refereed)
    Abstract [en]

    Low-Middle-Income-Countries (LMICs) are beginning to adopt national health data warehousing (NHDWs) for making strategic decisions and for improving health outcomes. Given the numerous challenges likely to be faced in establishment of NHDWs by LMICs, it is prudent that evaluations are done in relation to the data warehouses (DWs), in order to identify and mitigate critical issues that arise. When critic issues are not identified, DWs are prone to suboptimal implementation with compromised outcomes. Despite the fact that several publications exist on evaluating DWs, evaluations specific to health data warehouses are scanty, with almost none evaluating NHDWs more so in LMICs. This paper uses a systematic approach guided by an evaluation framework to identify critical issues to be considered in evaluating Kenyas NHDW.

  • 38.
    Gesicho, Milka
    et al.
    Univ Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Task-Based Approach Recommendations to Enhance Data Visualization in the Kenya National Health Data Warehouse2019In: WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1, SPRINGER , 2019, Vol. 68, no 1, p. 467-470Conference paper (Refereed)
    Abstract [en]

    The health sector still lags behind in development of data visualization tools due to the complex nature of health data. Furthermore, due to the volume, velocity and veracity of health data consolidated from various sources, re-presenting them in a way that promotes decision-making while supporting various aspects of human interaction becomes even more challenging. With the plethora of research on improving visualization of integrated health data, focus is shifting from simple charts to novel ways of data re-presentation. Literature also suggests the need for an in-depth exploration on aligning visualizations to tasks, context, and appropriate cognition aspects. We conducted a field study at the Kenya National Health Data Warehouse (KNHDW) in the month of July 2017 to identify the techniques and practices used to visualize data. Two salient tasks performed in the KNHDW were identified in order to explore possibilities of visualizing the data. We then adopted a task-based approach in developing recommendations based on categorical data. These recommendations include (1) use of visualization approaches that promote proper space utilization, and (2) use of leverage points that influence aspects of human cognition process. In addition, the proposed visualizations enable potential users to get a new experience with the data and explore possibilities for visualization. Nevertheless, these recommendations are by no means exhaustive but aim at encouraging best practice in health data visualization in the KNHDW.

  • 39.
    Gharehbaghi, A.
    et al.
    Malardalen Univ, Sweden.
    Sepehri, Amir A.
    CAPIS Biomed Res and Dept Ctr, Belgium.
    Linden, Maria
    Malardalen Univ, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    A Hybrid Machine Learning Method for Detecting Cardiac Ejection Murmurs2018In: EMBEC and NBC 2017, SPRINGER-VERLAG SINGAPORE PTE LTD , 2018, Vol. 65, p. 787-790Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for detecting cardiac ejection murmurs from other pathological and physiological heart murmurs in children. The proposed method combines a hybrid model and a time growing neural network for an improved detection even in mild condition. Children with aortic stenosis and pulmonary stenosis comprised the patient category against the reference category containing mitral regurgitation, ventricular septal defect, innocent murmur and normal (no murmur) conditions. In total, 120 referrals to a children University hospital participated to the study after giving their informed consent. Confidence interval of the accuracy, sensitivity and specificity is estimated to be 87.2%-88.8%, 83.4%-86.9% and 88.3%-90.0%, respectively.

  • 40.
    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.

  • 41.
    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.

  • 42.
    Gharehbaghi, Arash
    et al.
    Malardalen University, Sweden.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Science & Engineering.
    Nylander, Eva
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Janerot-Sjoberg, Birgitta
    Karolinska Institute, Sweden; Karolinska University Hospital, Sweden; KTH Royal Institute Technology, Sweden.
    Ekman, Inger
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Linden, Maria
    Malardalen University, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. University of Bergen, Norway.
    A Hybrid Model for Diagnosing Sever Aortic Stenosis in Asymptomatic Patients using Phonocardiogram2015In: WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, 2015, VOLS 1 AND 2, Springer, 2015, Vol. 51, p. 1006-1009Conference paper (Refereed)
    Abstract [en]

    This study presents a screening algorithm for severe aortic stenosis (AS), based on a processing method for phonocardiographic (PCG) signal. The processing method employs a hybrid model, constituted of a hidden Markov model and support vector machine. The method benefits from a preprocessing phase for an enhanced learning. The performance of the method is statistically evaluated using PCG signals recorded from 50 individuals who were referred to the echocardiography lab at Linkoping University hospital. All the individuals were diagnosed as having a degree of AS, from mild to severe, according to the echocardiographic measurements. The patient group consists of 26 individuals with severe AS, and the rest of the 24 patients comprise the control group. Performance of the method is statistically evaluated using repeated random sub sampling. Results showed a 95% confidence interval of (80.5%-82.8%)/(77.8%-80.8%) for the accuracy/sensitivity, exhibiting an acceptable performance to be used as decision support system in the primary healthcare center.

  • 43.
    Gharehbaghi, Arash
    et al.
    School of Innovation, Design and Technology, Mälardalen University, Västerås, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography2018In: Data, Informatics and Technology: An Inspiration for Improved Healthcare / [ed] Arie Hasman, Parisis Gallos, Joseph Liaskos, Mowafa S. Househ, John Mantas, IOS Press, 2018, Vol. 251, p. 157-160Chapter in book (Refereed)
    Abstract [en]

    This paper presents a method for exploring structural risk of any artificial intelligence-based method in bioinformatics, the A-Test method. This method provides a way to not only quantitate the structural risk associated with a classification method, but provides a graphical representation to compare the learning capacity of different classification methods. Two different methods, Deep Time Growing Neural Network (DTGNN) and Hidden Markov Model (HMM), are selected as two classification methods for comparison. Time series of heart sound signals are employed as the case study where the classifiers are trained to learn the disease-related changes. Results showed that the DTGNN offers a superior performance both in terms of the capacity and the structural risk. The A-Test method can be especially employed in comparing the learning methods with small data size.

  • 44.
    Gharehbaghi, Arash
    et al.
    Malardalen Univ, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Sepehri, Amir A.
    CAPIS Biomed Res and Dev Ctr, Belgium.
    Extraction of Diagnostic Information from Phonocardiographic Signal Using Time-Growing Neural Network2019In: WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 3, SPRINGER , 2019, Vol. 68, no 3, p. 849-853Conference paper (Refereed)
    Abstract [en]

    This paper presents an original method for extracting medical information from a heart sound recording, so called Phonocardiographic (PCG) signal. The extracted information is employed by a binary classifier to distinguish between stenosis and regurgitation murmurs. The method is based on using our original neural network, the Time-Growing Neural Network (TGNN), in an innovative way. Children with an obstruction on their semilunar valve are considered as the patient group (PG) against a reference group (RG) of children with a regurgitation in their atrioventricular valve. PCG signals were collected from 55 children, 25/30 from the PG/RG, who referred to the Children Medical Center of Tehran University. The study was conducted according to the guidelines of Good Clinical Practices and the Declaration of Helsinki. Informed consents were obtained for all the patients prior to the data acquisition. The accuracy and sensitivity of the method was estimated to be 85% and 80% respectively, exhibiting a very good performance to be used as a part of decision support system. Such a decision support system can improve the screening accuracy in primary healthcare centers, thanks to the innovative use of TGNN.

  • 45.
    Gharehbaghi, Arash
    et al.
    Malardalen Univ, Sweden.
    Sepehri, Amir A.
    CAPIS Biomed Res and Dev Ctr, Belgium.
    Linden, Maria
    Malardalen Univ, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Intelligent Phonocardiography for Screening Ventricular Septal Defect Using Time Growing Neural Network2017In: INFORMATICS EMPOWERS HEALTHCARE TRANSFORMATION, IOS PRESS , 2017, Vol. 238, p. 108-111Conference paper (Refereed)
    Abstract [en]

    This paper presents results of a study on the applicability of the intelligent phonocardiography in discriminating between Ventricular Spetal Defect (VSD) and regurgitation of the atrioventricular valves. An original machine learning method, based on the Time Growing Neural Network (TGNN), is employed for classifying the phonocardiographic recordings collected from the pediatric referrals to a children hospital. 90 individuals, 30 VSD, 30 with the valvular regurgitation, and 30 healthy subjects, participated in the study after obtaining the informed consents. The accuracy and sensitivity of the approach is estimated to be 86.7% and 83.3%, respectively, showing a good performance to be used as a decision support system.

  • 46.
    Gharehbaghi, Arash
    et al.
    Department of Innovation, Design and Technology, Mälardalen University, Västerås, Sweden.
    Sepehri, Amir A.
    CAPIS Biomedical Research and Development Center, Mon, Belgium.
    Lindén, Maria
    Department of Innovation, Design and Technology, Mälardalen University, Västerås, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Department of Information Science and Media Studies, University of Bergen, Norway.
    Intelligent Phonocardiography for Screening Ventricular Septal Defect Using Time Growing Neural Network2017In: Informatics Empowers Healthcare Transformation / [ed] Househ M.S.,Mantas J.,Hasman A.,Gallos P., IOS Press, 2017, Vol. 238, p. 108-111Conference paper (Refereed)
    Abstract [en]

    This paper presents results of a study on the applicability of the intelligent phonocardiography in discriminating between Ventricular Spetal Defect (VSD) and regurgitation of the atrioventricular valves. An original machine learning method, based on the Time Growing Neural Network (TGNN), is employed for classifying the phonocardiographic recordings collected from the pediatric referrals to a children hospital. 90 individuals, 30 VSD, 30 with the valvular regurgitation, and 30 healthy subjects, participated in the study after obtaining the informed consents. The accuracy and sensitivity of the approach is estimated to be 86.7% and 83.3%, respectively, showing a good performance to be used as a decision support system.

  • 47.
    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.

  • 48.
    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)
  • 49.
    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)
  • 50.
    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)
12 1 - 50 of 93
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf