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  • 1.
    Arkad, Kristina
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ludwigs, Ulf
    Södersjukhuset Stockholm.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    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.
    Medical Logic Module (MLM) representation of knowledge in a ventilator treatment advisory system1991In: International Journal of Clinical Monitoring and Computing, ISSN 0167-9945, E-ISSN 2214-7314, Vol. 8, p. 43-48Article in journal (Refereed)
  • 2.
    Arkad, Kristina
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    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.
    Jean, Francois-Christophe
    Medical Informatics Dept Broussais University hospital, Paris.
    Degoulet, Patrice
    Medical Informatics Dept Broussais University Hospital, Paris.
    Integration of data driven decision support into the HELIOS environment1994In: International journal of bio-medical computing, ISSN 0020-7101, Vol. 34, p. 195-205Article in journal (Refereed)
  • 3.
    Collste, Göran
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Religion and Culture, Center for Applied Ethics.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A decision support system for diabetes care: Ethical aspects1999In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 38, p. 313-316Article in journal (Refereed)
  • 4.
    Gill, Hans
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ludwigs, Ulf
    Södersjukhuset Stockholm.
    Matell, George
    Södersjukhuset Stockholm.
    Rudowski, Robert
    Polish Academy of Sciences Warszawa.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    ström, Christer
    Siemens Elema Solna.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Integrating knowledge-based technology into computer aided ventilation systems1990In: International Journal of Clinical Monitoring and Computing, ISSN 0167-9945, E-ISSN 2214-7314, Vol. 7Article in journal (Refereed)
  • 5.
    Johansson, Bo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    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.
    Database and knowledge base integration - A data mapping method for Arden Syntax knowledge modules1996In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 35, p. 302-309Article in journal (Refereed)
  • 6.
    Johansson, Bo
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    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.
    Database and knowledge base integration in decision support systems1996In: AMIA 1996,1996, Washington: Hanley & belfus , 1996, p. 249-Conference paper (Refereed)
  • 7.
    Karlsson, Daniel
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ekdahl, Christer
    Linköping University, Department of Molecular and Clinical Medicine.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Forsum, Urban
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Clinical Microbiology. Östergötlands Läns Landsting, Centre for Laboratory Medicine, Department of Clinical Microbiology.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A WWW-based decision-support system using medical logic modules and hypertext1996In: Medical Informatics Europe 96,1996, Amsterdam: IOS Press , 1996, p. 93-Conference paper (Refereed)
  • 8.
    Karlsson, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ekdahl, Christer
    Linköping University, Department of Clinical and Experimental Medicine, Infectious Diseases. Linköping University, Faculty of Health Sciences.
    Wigertz, Ove
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Forsum, Urban
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Microbiology. Linköping University, Faculty of Health Sciences.
    Extended telemedical consultation using Arden Syntax based decision support, hypertext and WWW technique1997In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 36, no 2, p. 108-114Article in journal (Refereed)
    Abstract [en]

    There is an obvious need for geographic distribution of expert knowledge among several health care units without increasing the cost of on-site expertise in locations where health care is provided. This paper describes the design of a knowledge-based decision-support system for extended consultation in clinical medicine. The system is based on Arden Syntax for Medical Logic Modules and hypertext using World Wide Web technology. It provides advice and explanations regarding the given advice. The explanations are presented in a hypertext format allowing the user to browse related information and to verify the relevance of the given advice. The system is intended to be used in a closed local network. With special precautions regarding issues of safety and patient security, the system can be used over wider areas such as in rural medicine. A prototype has been developed in the field of clinical microbiology and infectious diseases regarding infective endocarditis.

  • 9.
    Krusinska, Ewa
    et al.
    University of Technology Wroclaw, Poland.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    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.
    Chowdhury, Shamsul
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A systematic approach to knowledge extraction and representation for medical decision support1993In: Int Open Workshop on Knowledge acquisition, representation and processing,1993, 1993, p. 47-Conference paper (Refereed)
  • 10.
    Krusinska, Ewa
    et al.
    Technical University of Wroclaw Poland.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    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.
    Chowdhury, Shamsul
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A systematic approach to knowledge extraction and representation for medical decision support1994In: Foundations of computing and decision sciences, ISSN 0867-6356, Vol. 19, no 1-2, p. 71-88Article in journal (Refereed)
  • 11.
    Lind, Leili
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Sundvall, Erik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Karlsson, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Requirements and prototyping of a home health care application based on emerging JAVA technology.2002In: International Journal of Medical Informatics, ISSN 1386-5056, Vol. 68, no 1-3, p. 129-139Article in journal (Refereed)
    Abstract [en]

    IT support for home health care is an expanding area within health care IT development. Home health care differs from other in- or outpatient care delivery forms in a number of ways, and thus, the introduction of home health care applications must be based on a rigorous analysis of necessary requirements to secure safe and reliable health care. This article reports early experiences from the development of a home health care application based on emerging technologies. A prototype application for the follow-up of diabetes patients is presented and discussed in relation to a list of general requirements on home health care applications.

  • 12.
    Lyth, Johan
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Andersson, Swen-Olof
    Department of Urology, Örebro University Hospital, Örebro.
    Andrén, Ove
    Department of Urology, Örebro University Hospital, Örebro.
    Johansson, Jan-Erik
    Department of Urology, Örebro University Hospital, Örebro.
    Carlsson, Per
    Linköping University, Department of Medical and Health Sciences, Health Technology Assessment and Health Economics. Linköping University, Faculty of Health Sciences.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A decision support model for cost-effectiveness of radical prostatectomy in localized prostate cancer2012In: Scandinavian Journal of Urology and Nephrology, ISSN 0036-5599, E-ISSN 1651-2065, Vol. 46, no 1, p. 19-25Article in journal (Refereed)
    Abstract [en]

    Objective. This study aimed to develop a probabilistic decision support model to calculate the lifetime incremental cost-effectiveness ratio (ICER) between radical prostatectomy and watchful waiting for different patient groups. Material and methods. A randomized trial (SPCG-4) provided most data for this study. Data on survival, costs and quality of life were inputs in a decision analysis, and a decision support model was developed. The model can generate cost-effectiveness information on subgroups of patients with different characteristics. Results. Age was the most important independent factor explaining cost-effectiveness. The cost-effectiveness value varied from 21 026 Swedish kronor (SEK) to 858 703 SEK for those aged 65 to 75 years, depending on Gleason scores and prostate-specific antigen (PSA) values. Information from the decision support model can support decision makers in judging whether or not radical prostatectomy (RP) should be used to treat a specific patient group. Conclusions. The cost-effectiveness ratio for RP varies with age, Gleason scores, and PSA values. Assuming a threshold value of 200 000 SEK per quality-adjusted life-year (QALY) gained, for patients aged ≤70 years the treatment was always cost-effective, except at age 70, Gleason 0–4 and PSA ≤10. Using the same threshold value at age 75, Gleason 7–9 (regardless of PSA) and Gleason 5–6 (with PSA >20) were cost-effective. Hence, RP was not perceived to be cost-effective in men aged 75 years with low Gleason and low PSA. Higher threshold values for patients with clinically localized prostate cancer could be discussed.

  • 13.
    Nyström, Mikael
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Örman, Håkan
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Lind, Leili
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Sundvall, Erik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Karlsson, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Det krävs en riktad satsning på e-hälsa2016In: Dagens medicin, ISSN 1104-7488, no 18, p. 22-Article in journal (Other (popular science, discussion, etc.))
  • 14. Pirnejad, H.
    et al.
    Bal, R.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    The nature of unintended effects of health information systems concerning patient safety: A systematic review with thematic synthesis.2010Conference paper (Refereed)
    Abstract [en]

    In order to understand the nature and causes through which Health Information Systems (HIS) can affect patient safety negatively, a systematic review with thematic synthesis of the qualitative studies was performed. 26 papers met our criteria and were included into content analysis. 40 error contributing factors in working with HIS were recognized. Upon which, 4 main categories of contributing factors were defined. Analysis of the semantic relation between contributing reasons and common types of errors in healthcare practice revealed 6 mechanisms that can function as secondary contributing reasons. Results of this study can support care providers, system designers, and system implementers to avoid unintended negative effects for patient safety.

  • 15.
    Razavi, Amir R
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysis2008In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 8, no 41Article in journal (Refereed)
    Abstract [en]

    Background: The guideline for postmastectomy radiotherapy (PMRT), which is prescribed to reduce recurrence of breast cancer in the chest wall and improve overall survival, is not always followed. Identifying and extracting important patterns of non-compliance are crucial in maintaining the quality of care in Oncology.

    Methods: Analysis of 759 patients with malignant breast cancer using decision tree induction (DTI) found patterns of non-compliance with the guideline. The PMRT guideline was used to separate cases according to the recommendation to receive or not receive PMRT. The two groups of patients were analyzed separately. Resulting patterns were transformed into rules that were then compared with the reasons that were extracted by manual inspection of records for the non-compliant cases.

    Results: Analyzing patients in the group who should receive PMRT according to the guideline did not result in a robust decision tree. However, classification of the other group, patients who should not receive PMRT treatment according to the guideline, resulted in a tree with nine leaves and three of them were representing non-compliance with the guideline. In a comparison between rules resulting from these three non-compliant patterns and manual inspection of patient records, the following was found:

    In the decision tree, presence of perigland growth is the most important variable followed by number of malignantly invaded lymph nodes and level of Progesterone receptor. DNA index, age, size of the tumor and level of Estrogen receptor are also involved but with less importance. From manual inspection of the cases, the most frequent pattern for non-compliance is age above the threshold followed by near cut-off values for risk factors and unknown reasons.

    Conclusion: Comparison of patterns of non-compliance acquired from data mining and manual inspection of patient records demonstrates that not all of the non-compliances are repetitive or important. There are some overlaps between important variables acquired from manual inspection of patient records and data mining but they are not identical. Data mining can highlight non-compliance patterns valuable for guideline authors and for medical audit. Improving guidelines by using feedback from data mining can improve the quality of care in oncology.

  • 16.
    Razavi, Amir Reza
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Stål, Olle
    Linköping University, Department of Clinical and Experimental Medicine, Oncology . Linköping University, Faculty of Health Sciences.
    Sundquist, Marie
    Department of Surgery, County Hospital, Kalmar, Sweden.
    Thorstenson, Sten
    Department of Pathology, County Hospital, Kalmar, Sweden.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    The South-East Swedish Breast Cancer Study Group,
    Exploring cancer register data to find risk factors for recurrence of breast cancer: Application of Canonical Correlation Analysis2005In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, Vol. 5, no 29, p. 29-35Article in journal (Refereed)
    Abstract [en]

    Background

    A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time.

    One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model.

    Methods

    Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built.

    Results

    The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2–4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor.

    Conclusion

    In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones.

  • 17.
    Razavi, Amir Reza
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer2007In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 129, p. 591-597Article in journal (Refereed)
    Abstract [en]

    Postmastectomy radiotherapy (PMRT) is prescribed in order to reduce the local recurrence of breast cancer and improve overall survival. A guideline supports the trade-off between benefits and adverse effects of PMRT. However, this guideline is not always followed in practice. This study tries to find a method for revealing patterns of non-compliance between the actual treatment and the PMRT guideline.

    Data from breast cancer patients admitted to Linköping University Hospital between 1990 and 2000 were analyzed in this study. Cases that were not treated in accordance with the guideline were selected and analyzed by decision tree induction (DTI). Thereafter, four resulting rules, as representations for groups of patients, were compared to the guideline.

    Finding patterns of non-compliance with guidelines by means of rules can be an appropriate alternative to manual methods, i.e. a case-by-case comparison when studying very large datasets. The resulting rules can be used in a knowledge base of a guideline-based decision support system to alert when inconsistencies with the guidelines may appear.

  • 18.
    Razavi, Amir Reza
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    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.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A data mining approach to build a predictive model for breast cancer recurrence2006In: Annual Workshop of the Swedish Intelligence Society SAIS2006,2006, 2006, p. 51-55Conference paper (Other academic)
    Abstract [en]

        

  • 19.
    Razavi, Amir Reza
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A Data Pre-processing Method to Increase Efficiency and Accuracy in Data Mining2005In: 10th Conference on Artificial Intelligence in Medicine, AIME2005 - Aberdeen, UK / [ed] Miksch, Silvia, Hunter, Jim, Keravnou, Elpida, 2005, p. 434-443Conference paper (Other academic)
    Abstract [en]

    In medicine, data mining methods such as Decision Tree Induction (DTI) can be trained for extracting rules to predict the outcomes of new patients. However, incompleteness and high dimensionality of stored data are a problem. Canonical Correlation Analysis (CCA) can be used prior to DTI as a dimension reduction technique to preserve the character of the original data by omitting non-essential data. In this study, data from 3949 breast cancer patients were analysed. Raw data were cleaned by running a set of logical rules. Missing values were replaced using the Expectation Maximization algorithm. After dimension reduction with CCA, DTI was employed to analyse the resulting dataset. The validity of the predictive model was confirmed by ten-fold cross validation and the effect of pre-processing was analysed by applying DTI to data without pre-processing. Replacing missing values and using CCA for data reduction dramatically reduced the size of the resulting tree and increased the accuracy of the prediction of breast cancer recurrence.

  • 20.
    Razavi, Amir Reza
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    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.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Canonical correlation analysis for data reduction in data mining applied to predictive models for breast cancer recurrence2005In: The XIXth International Congress of the European Federation for Medical Informatics,2005, Amsterdam: IOSPress , 2005, p. 175-180Conference paper (Refereed)
    Abstract [en]

    Data mining methods can be used for extracting specific medical knowledge such as important predictors for recurrence of breast cancer in pertinent data material. However, when there is a huge quantity of variables in the data material it is first necessary to identify and select important variables. In this study we present a preprocessing method for selecting important variables in a dataset prior to building a predictive model. In the dataset, data from 5787 female patients were, analysed. To cover more predictors and obtain a better assessment of the outcomes, data were retrieved from three different registers: the regional breast cancer, tumour markers, and cause of death registers. After retrieving information about selected predictors and outcomes from the different registers, the raw data were cleaned by running different logical rules. Thereafter, domain experts selected predictors assumed to be important regarding recurrence of breast cancer. After that, Canonical Correlation Analysis (CCA) was applied as a dimension reduction technique to preserve the character of the original data. Artificial Neural Network (ANN) was applied to the resulting dataset for two different analyses with the same settings. Performance of the predictive models was confirmed by ten-fold cross validation. The results showed an increase in the accuracy of the prediction and reduction of the mean absolute error.

  • 21.
    Razavi, Amir Reza
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    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.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer2007In: MEDINFO 2007: PROCEEDINGS OF THE 12TH WORLD CONGRESS ON HEALTH (MEDICAL) INFORMATICS, PTS 1 AND 2, IOS Press, 2007, p. 591-595Conference paper (Refereed)
    Abstract [en]

    Postmastectomy radiotherapy (PAMT) is prescribed in order to reduce the local recurrence of breast cancer and improve overall survival. A guideline supports the trade-off between benefits and adverse effects of PMRT However, this guideline is not always followed in practice. This study tries to find a method for revealing patterns of noncompliance between the actual treatment and the PMRT guideline.Data from breast cancer patients admitted to Linkoping University Hospital between 1990 and 2000 were analyzed in this study. Cases that were not treated in accordance with the guideline were selected and analyzed by decision tree induction (DTI). Thereafter, four resulting rules, as representations for groups of patients, were compared to the guideline.Finding patterns of non-compliance with guidelines by means of rules can be an appropriate alternative to manual methods, i.e. a case-by-case comparison when studying very large datasets. The resulting rules can be used in a knowledge base of a guideline-based decision support system to alert when inconsistencies with the guidelines may appear.

  • 22.
    Razavi, Amir Reza
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Predicting metastasis in breast cancer: comparing a decision tree with domain experts2007In: Journal of Medical Systems, ISSN 0148-5598, Vol. 31, no 4, p. 263-273Article in journal (Refereed)
    Abstract [en]

    Breast malignancy is the second most common cause of cancer death among women in Western countries. Identifying high-risk patients is vital in order to provide them with specialized treatment. In some situations, such as when access to experienced oncologists is not possible, decision support methods can be helpful in predicting the recurrence of cancer. Three thousand six hundred ninety-nine breast cancer patients admitted in south-east Sweden from 1986 to 1995 were studied. A decision tree was trained with all patients except for 100 cases and tested with those 100 cases. Two domain experts were asked for their opinions about the probability of recurrence of a certain outcome for these 100 patients. ROC curves, area under the ROC curves, and calibration for predictions were computed and compared. After comparing the predictions from a model built by data mining with predictions made by two domain experts, no significant differences were noted. In situations where experienced oncologists are not available, predictive models created with data mining techniques can be used to support physicians in decision making with acceptable accuracy.

  • 23.
    Razavi, Amir Reza
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Nyström, Mikael
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Stachowicz, Marian S.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    An approach for generating fuzzy rules from decision trees2006In: Ubiquity: Technologies for Better Health in Aging Societies - Proceedings of MIE2006 / [ed] Arie Hasman, Reinhold Haux, Johan van der Lei, Etienne De Clercq, Francis Roger-France, IOS Press , 2006, p. 581-586Conference paper (Refereed)
    Abstract [en]

    Identifying high-risk breast cancer patients is vital both for clinicians and for patients. Some variables for identifying these patients such as tumor size are good candidates for fuzzification. In this study, Decision Tree Induction (DTI) has been applied to 3949 female breast cancer patients and crisp If-Then rules has been acquired from the resulting tree. After assigning membership functions for each variable in the crisp rules, they were converted into fuzzy rules and a mathematical model was constructed. One hundred randomly selected cases were examined by this model and compared with crisp rules predictions. The outcomes were examined by the area under the ROC curve (AUC). No significant difference was noticed between these two approaches for prediction of recurrence of breast cancer. By soft discretization of variables according to resulting rules from DTI, a predictive model, which is both more robust to noise and more comprehensible for clinicians, can be built.

  • 24.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Design, implementation and evaluation of a knowledge-based system to support ventilator therapy management1993Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    A good deal of research has been directed toward developing medical knowledge-based systems to assist the health care provider in both diagnostic and management decisions. Different studies by various groups have resulted in clinically useful knowledge-based systems with great potential for supporting decision-making, but due to a lack of methodology in the clinical integration and evaluation of these systems, only a few of them are in regular use. This thesis is devoted to the following aspects of knowledge-based system development:

    • Knowledge Representarion: Representing knowledge and mimicking the decision making behavior of domain experts is a central problem in the development of medical knowledge-based systems. The chosen representation scheme should cover all pieces of the domain knowledge. Reusability and shareability of the knowledge are other desirable features, since the development of useful knowledge bases is a very time and cost consuming process.
    • Knowledge Acquisition: There are a variety of knowledge acquisition techniques, but all techniques are not suitable for all domains. A successful outcome requires comprehensive knowledge in the knowledge-base. The quality of expert knowledge in the knowledge-base determines the quality of the system.
    • Knowledge-Base Maintenance: Verification and maintenance of the knowledge-base by the domain experts themselves is the main issue here. Since knowledge develops continuously, it is mandatory that the knowledge-base is updateable. The knowledge-base contents should be correct and free from redundancy and inconsistency.
    • lntegration: Integrating the system into the real environment, particularly with real patient data, constitutes a critical step, because prototype environments often differ from the clinical setting. A knowledge-based system can hardly be useful if it cannot be integrated with other applications in the real environment.
    • Evaluation: The evaluation of medical decision-support systems is important, and it is also difficult because there is no generally accepted methodology for carrying out this evaluation. The major aspect in the evaluation of a medical knowledge-based system is to find out whether the system is safe and legal, and to study the impact of the system on patients and the organization.

    This thesis examines and discusses the aforementioned factors based on experiences from the design, development, implementation and evaluation of VentEx, a knowledge-based decisionsupport and monitoring system we have built and applied in ventilator therapy. Our experience covers the whole development process from the prototype to an integrated on-line system. A hybrid knowledge representation has been used and a domain-specific knowledge acquisition tool (KAVE) equipped with a simulator has been developed. Real patient data has been used to validate the knowledge-base and a study to measure the impact of the system is ongoing. Evaluation results indicate a high consensus between the doctors and VentEx according to a "gold " standard.

  • 25.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knowledge acquisition and refinement for a domain-specific expert system1990Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Tools for developing expert systems (knowledge-based systems) exist and are being used increasingly. However, many such tools have poor support for knowledge base management. Three aspects are essential in the development of knowledge base management tools, namely knowledge representation, knowledge acquisition and knowledge refinement.

    This thesis discusses knowledge base management tools and reports an implementation for knowledge acquisition and refinement in a decision support system for artificial ventilation. The tool (KA VE) is based on a domain model and has facilities for entering, editing and refinement of the domain knowledge base. Morever an attached simulator and a knowledge editor support verification and refinement. KA VE also supports inconsistency and redundancy checking in the knowledge base.

    Prelirninary experiences show that the tool makes knowledge verbalization faster than is possible without computer support. The graphical interface and the rule description format have a good readability and have been used by domain experts to validate and tune the artificial ventilation knowledge base.

  • 26.
    Shahsavar, Nosrat
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ludwigs, Ulf
    Södersjukhuset Stockholm.
    Carstensen, A
    Södersjukhuset Stockholm.
    Larsson, H
    Dept of Med Engineering Huddinge sjukhus.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Matell, George
    Södersjukhuset Stockholm.
    VentEx: an on-line knowledge-based system to support ventilator management1994In: Technology and Health Care, ISSN 0928-7329, Vol. 1, p. 233-243Article in journal (Refereed)
  • 27.
    Shahsavar, Nosrat
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, 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.
    Frostell, Claes
    Danderyds Hospital .
    Matell, George
    Södersjukhuset Stockholm.
    Ludwigs, Ulf
    Södersjukhuset Stockholm.
    KAVE: a tool for knowledge acquisition to support artificial ventilation1991In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 34, p. 115-123Article in journal (Refereed)
  • 28.
    Shahsavar, Nosrat
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ludwigs, Ulf
    Södersjukhuset Stockholm.
    Blomqvist, Hans
    Danderyds sjukhus .
    Gill, 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.
    Matell, George
    Södersjukhuset Stockholm.
    Evaluation of a knowledge-based decision-support system for ventilator therapy management1995In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 7, p. 37-52Article in journal (Refereed)
  • 29.
    Shahsavar, Nosrat
    et al.
    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.
    Development and evaluation of knowledge-based system to support ventilator therapy management1995In: Conference on Artificial Intelligence in Medicine Europe, AIME95,1995, Springer , 1995, p. 433-Conference paper (Refereed)
  • 30.
    Wigertz, Ove
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Chowdhury, Shamsul
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Arkad, Kristina
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Johansson, Bo
    IMT LiU.
    Validation of medical logics in the Arden syntax knowledge representation1992In: MEDICON92 Mediterrean conference on medical and biological engineering,1992, Pisa: AREADI RICERCA , 1992, p. 1051-Conference paper (Refereed)
  • 31.
    Wigertz, Ove
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Hripcsak, George
    Columbia Presbyterian Medical Center New York.
    Shahsavar, Nosrat
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Bågenholm, Per
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Gill, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Data-driven medical knowled-based systems based on Arden Syntax1994In: Knowledge and Decisions in Health Telematics / [ed] P. Barahona, J.P. Christensen, Amsterdam: IOS Press , 1994, p. 126-131Chapter in book (Other academic)
  • 32.
    Wigertz, Ove
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Jönsson, Kjell-Åke
    Dept of Medicine Linköping University Hospital.
    Arkad, Kristina
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ohlsson, Per
    IMT .
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knowledge representation for an anticoagulant therapy advisorArkad1993In: Int Congress of the European Federation for Medical Informatics MIE93,1993, London, England: Freund Publishing House Ltd , 1993, p. 99-Conference paper (Refereed)
  • 33.
    Xiao-Ming, Gao
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Johansson, Bo
    IMT .
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Arkad, Kristina
    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.
    Hripcsak, George
    Columbia-Presbyterian Medical Center New York.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A method for realizing the Arden Syntax, a standard for medical knowledge representation, in building decision support systems1993In: Int Joint Conference on Artificial Intelligence 93,1993, 1993Conference paper (Refereed)
  • 34.
    Xiao-Ming, Gao
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Johansson, Bo
    IMT .
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Arkad, Kristina
    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.
    Pre-compiling medical logic modules into C++ in building medical decision support systems1993In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 41, p. 107-119Article in journal (Refereed)
  • 35.
    Xiao-Ming, Gao
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Arkad, Kristina
    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.
    Hripcsak, George
    Columbia-Presbyterian Medical Center NY, USA.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Design and function of medical knowledge editors for the Arden Syntax1992In: World Congress on Medical Informatics MEDINFO92,1992, Amsterdam: Elsevier Science Publ , 1992, p. 472-Conference paper (Refereed)
  • 36.
    Åhlfeldt, Hans
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Shahsavar, Nosrat
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Xiao-Ming, Gao
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Arkad, Kristina
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Johansson, Bo
    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.
    Data driven medical decision support based on Arden Syntax within the HELIOS environment1994In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 45, no suppl, p. S97-S106Article in journal (Refereed)
1 - 36 of 36
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