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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.
    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)
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  • 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.
    Farsirotos, Gina
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. University of Bergen, Norway.
    Information Technologies for Cognitive Decline2022In: : Advances in Informatics, Management and Technology in Healthcare / [ed] John Mantas, Parisis Gallos, Emmanouil Zoulias, Arie Hasman, Mowafa S. Househ, Marianna Diomidous, Joseph Liaskos, Martha Charalampidou, IOS Press, 2022, Vol. 295, p. 217-220Conference paper (Refereed)
    Abstract [en]

    Information technology (IT) is used to establish diagnoses and provide treatments for people with cognitive decline. The condition affects many before it becomes clear that more permanent changes, like dementia, could be noticed. Those who search for information are exposed to lots of information and different technologies which they need to make sense of and eventually use to help themselves. In this research, we have systematically analyzed the literature and information available on the Internet to systematically present methods used in diagnosing and treatment. We have also developed an artifact to help users obtain information with help of illustrations and text. The final user groups are all those for whom the cognitive decline is of concern. Medical professionals could be interested to direct their patients to use the artifact to gain information and keep learning at their own pace.

  • 36.
    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.

  • 37. 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)
  • 38.
    Gesicho, Milka B.
    et al.
    Univ Bergen, Norway; Moi Univ, Kenya.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Identifying barriers and facilitators in HIV-indicator reporting for different health facility performances: A qualitative case study2021In: PLOS ONE, E-ISSN 1932-6203, Vol. 16, no 2, article id e0247525Article in journal (Refereed)
    Abstract [en]

    Identifying barriers and facilitators in HIV-indicator reporting contributes to strengthening HIV monitoring and evaluation efforts by acknowledging contributors to success, as well as identifying weaknesses within the system that require improvement. Nonetheless, there is paucity in identifying and comparing barriers and facilitators in HIV-indicator data reporting among facilities that perform well and those that perform poorly at meeting reporting completeness and timeliness requirements. Therefore, this study aims to use a qualitative approach in identifying and comparing the current state of barriers and facilitators in routine reporting of HIV-indicators by facilities performing well, and those performing poorly in meeting facility reporting completeness and timeliness requirements to District Health Information Software2 (DHIS2). A multiple qualitative case study design was employed. The criteria for case selection was based on performance in HIV-indicator facility reporting completeness and timeliness. Areas of interest revolved around reporting procedures, organizational, behavioral, and technical factors. Purposive sampling was used to identify key informants in the study. Data was collected using semi-structured in-depth interviews with 13 participants, and included archival records on facility reporting performance, looking into documentation, and informal direct observation at 13 facilities in Kenya. Findings revealed that facilitators and barriers in reporting emerged from the following factors: interrelationship between workload, teamwork and skilled personnel, role of an EMRs system in reporting, time constraints, availability and access-rights to DHIS2, complexity of reports, staff rotation, availability of trainings and mentorship, motivation, availability of standard operating procedures and resources. There was less variation in barriers and facilitators faced by facilities performing well and those performing poorly. Continuous evaluations have been advocated within health information systems literature. Therefore, continuous qualitative assessments are also necessary in order to determine improvements and recurring of similar issues. These assessments have also complemented other quantitative analyses related to this study.

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  • 39.
    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.

  • 40.
    Gesicho, Milka B.
    et al.
    Department of Information Science and Media Studies, University of Bergen, Norway; Institute of Biomedical Informatics, Moi University, Kenya.
    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.
    Were, Martin C.
    Vanderbilt University Medical Center, USA; Institute of Biomedical Informatics, Moi University, Kenya.
    Health Facility Ownership Type and Performance on HIV Indicator Data Reporting in Kenya2020In: Digital Personalized Health and Medicine / [ed] Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott, IOS Press , 2020, Vol. 270, p. 1301-1302Conference paper (Refereed)
    Abstract [en]

    In low- and middle-income countries, private and public facilities tend to have highly variable characteristics, which might affect their performance in meeting reporting requirements mandated by ministries of health. There is conflicting evidence on which facility type performs better across various care dimensions, and only few studies exist to evaluate relative performance around nationally-mandated indicator reporting to Ministries of Health. In this study, we evaluated the relationship between facility ownership type and performance on HIV indicator data reporting, using the case of Kenya. We conducted Mann-Whitney U tests using HIV indicator data extracted from years 2011 to 2018 for all the counties in Kenya, from the District Health Information Software 2 (DHIS2). Results from the study reveal that public facilities have statistically significant better performance compared to private facilities, with an exception of year 2017 in reporting of counselling and testing, and prevention of mother-to-child transmission indicator categories.

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  • 41.
    Gesicho, Milka B.
    et al.
    Department of Information Science and Media Studies, University of Bergen, Norway; Institute of Biomedical Informatics, Moi University, Kenya.
    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.
    Were, Martin C.
    Vanderbilt University Medical Center, US; Institute of Biomedical Informatics, Moi University, Kenya.
    K-Means Clustering in Monitoring Facility Reporting of HIV Indicator Data: Case of Kenya2020In: The Importance of Health Informatics in Public Health during a Pandemic / [ed] John Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos, Emmanouil Zoulias, IOS Press , 2020, Vol. 272, p. 143-146Conference paper (Refereed)
    Abstract [en]

    Health management information systems (HMISs) in low- and middle-income countries have been used to collect large amounts of data after years of implementation, especially in support of HIV care services. National-level aggregate reporting data derived from HMISs are essential for informed decision-making. However, the optimal statistical approaches and algorithms for deriving key insights from these data are yet to be fully and adequately utilized. This paper demonstrates use of the k-means clustering algorithm as an approach in supporting monitoring of facility reporting and data-informed decision-making, using the case example of Kenya HIV national reporting data. Results reveal four homogeneous cluster categories that can be used in assessing overall facility performance and rating of that performance.

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  • 42.
    Gesicho, Milka
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. University of Bergen, Norway.
    Designing a Dashboard for HIV-data Reporting Performance by Facilities: Case Study of Kenya.2022In: Advances in Informatics, Management and Technology in Healthcare / [ed] John Mantas, Parisis Gallos, Emmanouil Zoulias, Arie Hasman, Mowafa S. Househ, Marianna Diomidous, Joseph Liaskos, Martha Charalampidou, IOS Press, 2022, Vol. 295, p. 238-241Conference paper (Refereed)
    Abstract [en]

    Health management information systems implemented in low-and middle-income countries (LMICs) have provided availability of HIV-data. As such, dashboards have become increasingly popular as they provide a potentially powerful avenue for deriving insights at glance. This promotes use of data for decision-making by various stakeholders such as Ministries of Health as well as international donor organizations. Nonetheless, despite the use of dashboards in LMICs, their potential may go unrealized with underutilization of good design principles. In various LMICs, health facilities are required to submit HIV-indicator data on time for its use in decision-making. Hence, dashboards can be utilized in assessing facility reporting performance overtime in order to identify where interventions are needed. In this study, we applied good design principles in developing a dashboard, which presents the performance of facilities in reporting HIV-indicator data overtime (2011–2018). Timeliness and completeness in reporting were used as performance indicators and were extracted from the District Health Information Software Version 2 (DHIS2) in Kenya. Results for the system usability scale used in evaluating the dashboard was 87, which meant the dashboard usability was good.

  • 43.
    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.

  • 44.
    Gesicho, Milka Bochere
    et al.
    Univ Bergen, Norway; Moi Univ, Kenya.
    Were, Martin Chieng
    Vanderbilt Univ, TN USA; Moi Univ, Kenya.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Data cleaning process for HIV-indicator data extracted from DHIS2 national reporting system: a case study of Kenya2020In: BMC Medical Informatics and Decision Making, E-ISSN 1472-6947, Vol. 20, no 1, article id 293Article in journal (Refereed)
    Abstract [en]

    Background The District Health Information Software-2 (DHIS2) is widely used by countries for national-level aggregate reporting of health-data. To best leverage DHIS2 data for decision-making, countries need to ensure that data within their systems are of the highest quality. Comprehensive, systematic, and transparent data cleaning approaches form a core component of preparing DHIS2 data for analyses. Unfortunately, there is paucity of exhaustive and systematic descriptions of data cleaning processes employed on DHIS2-based data. The aim of this study was to report on methods and results of a systematic and replicable data cleaning approach applied on HIV-data gathered within DHIS2 from 2011 to 2018 in Kenya, for secondary analyses. Methods Six programmatic area reports containing HIV-indicators were extracted from DHIS2 for all care facilities in all counties in Kenya from 2011 to 2018. Data variables extracted included reporting rate, reporting timeliness, and HIV-indicator data elements per facility per year. 93,179 facility-records from 11,446 health facilities were extracted from year 2011 to 2018. Van den Broeck et al.s framework, involving repeated cycles of a three-phase process (data screening, data diagnosis and data treatment), was employed semi-automatically within a generic five-step data-cleaning sequence, which was developed and applied in cleaning the extracted data. Various quality issues were identified, and Friedman analysis of variance conducted to examine differences in distribution of records with selected issues across eight years. Results Facility-records with no data accounted for 50.23% and were removed. Of the remaining, 0.03% had over 100% in reporting rates. Of facility-records with reporting data, 0.66% and 0.46% were retained for voluntary medical male circumcision and blood safety programmatic area reports respectively, given that few facilities submitted data or offered these services. Distribution of facility-records with selected quality issues varied significantly by programmatic area (p < 0.001). The final clean dataset obtained was suitable to be used for subsequent secondary analyses. Conclusions Comprehensive, systematic, and transparent reporting of cleaning-process is important for validity of the research studies as well as data utilization. The semi-automatic procedures used resulted in improved data quality for use in secondary analyses, which could not be secured by automated procedures solemnly.

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  • 45.
    Gesicho, Milka Bochere
    et al.
    Univ Bergen, Norway; Moi Univ, Kenya.
    Were, Martin Chieng
    Vanderbilt Univ, TN USA; Moi Univ, Kenya.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm2021In: BMC Medical Informatics and Decision Making, E-ISSN 1472-6947, Vol. 21, no 1, article id 6Article in journal (Refereed)
    Abstract [en]

    Background: The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV-indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. The aim of this study is to conduct a comprehensive assessment of the reporting status for HIV-indicators, from the time of DHIS2 implementation, using Kenya as a case study. Methods: A retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 and 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness facility reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters. Results: Based on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups. Conclusions: The identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.

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  • 46.
    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.

  • 47.
    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.

  • 48.
    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.

  • 49.
    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.

  • 50.
    Gharehbaghi, Arash
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    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.
    A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods2022In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 289, p. 132-135Article in journal (Refereed)
    Abstract [en]

    This paper presents an original method for studying the performance of the supervised Machine Learning (ML) methods, the A-Test method. The method offers the possibility of investigating the structural risk as well as the learning capacity of ML methods in a quantitating manner. A-Test provides a powerful validation method for the learning methods with small or medium size of the learning data, where overfitting is regarded as a common problem of learning. Such a condition can occur in many applications of bioinformatics and biomedical engineering in which access to a large dataset is a challengeable task. Performance of the A-Test method is explored by validation of two ML methods, using real datasets of heart sound signals. The datasets comprise of children cases with a normal heart condition as well as 4 pathological cases: aortic stenosis, ventricular septal defect, mitral regurgitation, and pulmonary stenosis. It is observed that the A[1]Test method provides further comprehensive and more realistic information about the performance of the classification methods as compared to the existing alternatives, the K-fold validation and repeated random sub-sampling.

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