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Shahsavar, Nosrat
Publications (10 of 36) Show all publications
Nyström, M., Örman, H., Lind, L., Sundvall, E., Shahsavar, N. & Karlsson, D. (2016). Det krävs en riktad satsning på e-hälsa. Dagens medicin (18), pp. 22
Open this publication in new window or tab >>Det krävs en riktad satsning på e-hälsa
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2016 (Swedish)In: Dagens medicin, ISSN 1104-7488, no 18, p. 22-Article in journal, News item (Other (popular science, discussion, etc.)) Published
Place, publisher, year, edition, pages
Stockholm: , 2016
Keyword
e-hälsa, vision, forskning
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:liu:diva-127704 (URN)
Available from: 2016-05-09 Created: 2016-05-09 Last updated: 2018-01-10
Lyth, J., Andersson, S.-O., Andrén, O., Johansson, J.-E., Carlsson, P. & Shahsavar, N. (2012). A decision support model for cost-effectiveness of radical prostatectomy in localized prostate cancer. Scandinavian Journal of Urology and Nephrology, 46(1), 19-25
Open this publication in new window or tab >>A decision support model for cost-effectiveness of radical prostatectomy in localized prostate cancer
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2012 (English)In: Scandinavian Journal of Urology and Nephrology, ISSN 0036-5599, E-ISSN 1651-2065, Vol. 46, no 1, p. 19-25Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
London, UK: Informa Healthcare, 2012
Keyword
cost-effectiveness, decision support, prostate cancer, radical prostatectomy, randomized trial, watchful waiting
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-70933 (URN)10.3109/00365599.2011.615759 (DOI)000299125800005 ()
Available from: 2011-09-21 Created: 2011-09-21 Last updated: 2017-12-08
Pirnejad, H., Bal, R. & Shahsavar, N. (2010). The nature of unintended effects of health information systems concerning patient safety: A systematic review with thematic synthesis.. In: : . Paper presented at 13th World Congress on Medical and Health Informatics, Medinfo 2010, Cape Town, South Africa (pp. 719-723). IOS Press
Open this publication in new window or tab >>The nature of unintended effects of health information systems concerning patient safety: A systematic review with thematic synthesis.
2010 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
IOS Press, 2010
Series
Studies in Health Technology and Informatics, ISSN 0926-9630
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-63587 (URN)10.3233/978-1-60750-588-4-719 (DOI)9781607505884 (ISBN)9781607505877 (ISBN)
Conference
13th World Congress on Medical and Health Informatics, Medinfo 2010, Cape Town, South Africa
Available from: 2010-12-22 Created: 2010-12-22 Last updated: 2014-09-17
Razavi, A. R., Gill, H., Åhlfeldt, H. & Shahsavar, N. (2008). Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysis. BMC Medical Informatics and Decision Making, 8(41)
Open this publication in new window or tab >>Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysis
2008 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 8, no 41Article in journal (Refereed) Published
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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-15222 (URN)
Note
Original publication: Amir R Razavi, Hans Gill, Hans Åhlfeldt and Nosrat Shahsavar, Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysis, 2008, BMC Medical Informatics and Decision Making, (8), 41.http://dx.doi.org/10.1186/1472-6947-8-41. Copyright: The authorsAvailable from: 2008-10-24 Created: 2008-10-24 Last updated: 2017-12-14Bibliographically approved
Razavi, A. R., Gill, H., Åhlfeldt, H. & Shahsavar, N. (2007). A Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer. Studies in Health Technology and Informatics, 129, 591-597
Open this publication in new window or tab >>A Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer
2007 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 129, p. 591-597Article in journal (Refereed) Published
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.

National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-12709 (URN)
Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2017-12-14
Razavi, A. R., Gill, H., Åhlfeldt, H. & Shahsavar, N. (2007). Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer. In: MEDINFO 2007: PROCEEDINGS OF THE 12TH WORLD CONGRESS ON HEALTH (MEDICAL) INFORMATICS, PTS 1 AND 2: . Paper presented at 12th World Congress on Health (Medical) Informatics (pp. 591-595). IOS Press
Open this publication in new window or tab >>Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer
2007 (English)In: MEDINFO 2007: PROCEEDINGS OF THE 12TH WORLD CONGRESS ON HEALTH (MEDICAL) INFORMATICS, PTS 1 AND 2, IOS Press, 2007, p. 591-595Conference paper, Published 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.

Place, publisher, year, edition, pages
IOS Press, 2007
Series
Studies in Health Technology and Informatics, ISSN 0926-9630
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-38015 (URN)41111 (Local ID)978-1-58603-774-1 (ISBN)41111 (Archive number)41111 (OAI)
Conference
12th World Congress on Health (Medical) Informatics
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-08-29
Razavi, A. R., Gill, H., Åhlfeldt, H. & Shahsavar, N. (2007). Predicting metastasis in breast cancer: comparing a decision tree with domain experts. Journal of Medical Systems, 31(4), 263-273
Open this publication in new window or tab >>Predicting metastasis in breast cancer: comparing a decision tree with domain experts
2007 (English)In: Journal of Medical Systems, ISSN 0148-5598, Vol. 31, no 4, p. 263-273Article in journal (Refereed) Published
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.

Keyword
Data mining, Decision tree induction (DTI), Breast cancer, Classification, Prediction, Domain expert, Decision support
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-12708 (URN)10.1007/s10916-007-9064-1 (DOI)
Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2009-05-12
Razavi, A. R., Gill, H., Åhlfeldt, H. & Shahsavar, N. (2006). A data mining approach to build a predictive model for breast cancer recurrence. In: Annual Workshop of the Swedish Intelligence Society SAIS2006,2006 (pp. 51-55).
Open this publication in new window or tab >>A data mining approach to build a predictive model for breast cancer recurrence
2006 (English)In: Annual Workshop of the Swedish Intelligence Society SAIS2006,2006, 2006, p. 51-55Conference paper, Published paper (Other academic)
Abstract [en]

    

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-34130 (URN)20874 (Local ID)20874 (Archive number)20874 (OAI)
Available from: 2009-10-10 Created: 2009-10-10
Razavi, A. R., Nyström, M., Stachowicz, M. S., Gill, H., Åhlfeldt, H. & Shahsavar, N. (2006). An approach for generating fuzzy rules from decision trees. In: Arie Hasman, Reinhold Haux, Johan van der Lei, Etienne De Clercq, Francis Roger-France (Ed.), Ubiquity: Technologies for Better Health in Aging Societies - Proceedings of MIE2006. Paper presented at The XXst International Congress of the European Federation for Medical Informatics (pp. 581-586). IOS Press
Open this publication in new window or tab >>An approach for generating fuzzy rules from decision trees
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2006 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
IOS Press, 2006
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 124
Keyword
Fuzzy Set Theory, Decision Tree Induction, Breast Cancer, Distant Metastasis
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-36666 (URN)000281143200082 ()17108580 (PubMedID)32105 (Local ID)978-1-58603-647-8 (ISBN)32105 (Archive number)32105 (OAI)
Conference
The XXst International Congress of the European Federation for Medical Informatics
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2018-03-26
Razavi, A. R., Gill, H., Åhlfeldt, H. & Shahsavar, N. (2005). A Data Pre-processing Method to Increase Efficiency and Accuracy in Data Mining. In: Miksch, Silvia, Hunter, Jim, Keravnou, Elpida (Ed.), 10th Conference on Artificial Intelligence in Medicine, AIME2005 - Aberdeen, UK: . Paper presented at 10th Conference on Artificial Intelligence in Medicine, AIME2005 - Aberdeen, UK (pp. 434-443).
Open this publication in new window or tab >>A Data Pre-processing Method to Increase Efficiency and Accuracy in Data Mining
2005 (English)In: 10th Conference on Artificial Intelligence in Medicine, AIME2005 - Aberdeen, UK / [ed] Miksch, Silvia, Hunter, Jim, Keravnou, Elpida, 2005, p. 434-443Conference paper, Published 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.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 3581
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-12707 (URN)10.1007/11527770_59 (DOI)978-3-540-27831-3 (ISBN)
Conference
10th Conference on Artificial Intelligence in Medicine, AIME2005 - Aberdeen, UK
Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2018-02-20
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